{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import time\n", "from scipy.optimize import differential_evolution, minimize, dual_annealing\n", "import matplotlib.pyplot as plt\n", "import concurrent.futures\n", "import threading, multiprocessing\n", "\n", "import sys \n", "sys.path.append(\"../\") \n", "import minionpy as mpy\n", "import minionpy.test_functions as mpytest\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Minimizing Basic Functions\n", "\n", "In this section, we minimize basic functions such as Sphere, Rosenbrock, and Rastrigin. The search space is shifted to prevent algorithms from converging near the origin, as some algorithms tend to favor that region.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The minimum of the function is \n", "\t x : [0.9999999999999998, 0.9999999999999997, 1.0, 1.0, 0.9999999999999993] \n", "\t f(x) : 160200.0\n" ] } ], "source": [ "def sphere(x) : \n", " x =np.asarray(x)-1\n", " return 100+np.sum(x**2)\n", "\n", "def rosenbrock(x):\n", " #if x[0]<-1 or x[0]>1 : print(x, \"bound violated\")\n", " x = np.asarray(x)-5.0\n", " return 100+np.sum(100 * (x[1:] - x[:-1]**2)**2 + (1 - x[:-1])**2)\n", "\n", "def rastrigin(x):\n", " x = np.asarray(x)-1.0\n", " A = 10\n", " return 100+A * len(x) + np.sum(x**2 - A * np.cos(2 * np.pi * x))\n", "\n", "#remember that in minion, objective function must be vectorized. Suppose you want to \n", "func = rosenbrock\n", "def objective_function(X) : \n", " \"\"\" \n", " Here, X is a list of x, where x is an input vector. \n", " \"\"\"\n", " return [func(x) for x in X] \n", "\n", "#Now minimize the function using minion \n", "dimension = 5 #set dimension of the problem\n", "maxevals = 1000 #number of function calls\n", "x0 = [[0.0]*dimension] # initial guesses\n", "\n", "min = mpy.Minimizer(func=objective_function, x0=x0, bounds=[(-1, 1)]*dimension, algo=\"ARRDE\", relTol=0.0, \n", " maxevals=10000, callback=None, seed=None, options={\"minimum_population_size\":4})\n", "result = min.optimize()\n", "print(\"The minimum of the function is \", \"\\n\\t x : \", result.x, \"\\n\\t f(x) : \", result.fun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Basic Test Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Minionpy provides a variety of test functions for evaluating optimization algorithms. The dictionary `testFunctionDict` below contains some of the fundamental functions available. Many of these functions have a global minimum at the origin, which can be problematic since some algorithms naturally converge toward it. To address this issue, CEC benchmark functions apply rotation and shifting to these basic functions, making them more representative of real-world optimization scenarios. \n", "\n", "The test functions in `minionpy.test_functions` are vectorized by default. They accept input as a 2D NumPy array.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "testFunctionDict = {\n", " \"sphere\" : mpytest.sphere, \n", " \"rosenbrock\" : mpytest.rosenbrock, \n", " \"rastrigin\" : mpytest.rastrigin, \n", " \"schaffer2\" : mpytest.schaffer2, \n", " \"griewank\" : mpytest.griewank, \n", " \"ackley\" : mpytest.ackley, \n", " \"zakharov\" : mpytest.zakharov, \n", " \"bent_cigar\" : mpytest.bent_cigar, \n", " \"levy\" : mpytest.levy, \n", " \"discus\" : mpytest.discus, \n", " \"drop_wave\" : mpytest.drop_wave, \n", " \"goldstein_price\" : mpytest.goldstein_price, \n", " \"exponential\" : mpytest.exponential,\n", " \"quartic\" : mpytest.quartic, \n", " \"hybrid_composition1\" : mpytest.hybrid_composition1,\n", " \"hybrid_composition2\" : mpytest.hybrid_composition2,\n", " \"hybrid_composition3\" : mpytest.hybrid_composition3,\n", " \"happy_cat\" : mpytest.happy_cat, \n", " \"michalewics\" : mpytest.michalewicz,\n", " \"scaffer6\" : mpytest.scaffer6, \n", " \"hcf\" : mpytest.hcf, \n", " \"grie_rosen\" : mpytest.grie_rosen,\n", " \"dixon_price\" : mpytest.dixon_price, \n", " \"eosom\" : mpytest.easom, \n", " \"hgbat\" : mpytest.hgbat, \n", " \"styblinski_tang\" : mpytest.styblinski_tang, \n", " \"step\" : mpytest.step, \n", " \"weierstrass\" : mpytest.weierstrass, \n", " \"sum_squares\" : mpytest.sum_squares\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The performance of different algorithms to minimize these functions can be compared as shown below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Function: sphere\n", "\tAlgorithm: DE f(x): 3.1013248e-13 Elapsed: 0.020 s\n", "\tAlgorithm: ABC f(x): 3.8409276e-07 Elapsed: 0.016 s\n", "\tAlgorithm: LSHADE f(x): 1.2420587e-09 Elapsed: 0.027 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 8.0453835e-12 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 4.9303807e-32 Elapsed: 0.020 s\n", "\tAlgorithm: jSO f(x): 4.3973511e-12 Elapsed: 0.028 s\n", "\tAlgorithm: j2020 f(x): 8.2324161e-13 Elapsed: 0.167 s\n", "\tAlgorithm: LSRTDE f(x): 2.1405603e-12 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 3.8072431e-08 Elapsed: 0.018 s\n", "\tAlgorithm: ARRDE f(x): 6.31914e-22 Elapsed: 0.016 s\n", "\tAlgorithm: GWO_DE f(x): 1.8723707e-06 Elapsed: 0.032 s\n", "\tAlgorithm: NelderMead f(x): 4.6709368e-30 Elapsed: 0.032 s\n", "\tAlgorithm: DA f(x): 1.7722074e-12 Elapsed: 0.106 s\n", "\tAlgorithm: L_BFGS_B f(x): 1.0668492e-13 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 3.5618553e-14 Elapsed: 0.015 s\n", "\tAlgorithm: DMSPSO f(x): 2.1524914e-15 Elapsed: 0.016 s\n", "\tAlgorithm: SPSO2011 f(x): 1.7546543e-08 Elapsed: 0.024 s\n", "\tAlgorithm: CMAES f(x): 2.0057539e-13 Elapsed: 0.011 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 1.8524367e-27 Elapsed: 0.041 s\n", "\tAlgorithm: RCMAES f(x): 1.4665005e-13 Elapsed: 0.025 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 5.8359322e-09 Elapsed: 0.030 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 6.9420299e-12 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): 4.2699916e-12 Elapsed: 0.328 s\n", "\n", "Function: rosenbrock\n", "\tAlgorithm: DE f(x): 8.4731067 Elapsed: 0.017 s\n", "\tAlgorithm: ABC f(x): 1.2322678 Elapsed: 0.011 s\n", "\tAlgorithm: LSHADE f(x): 2.756665 Elapsed: 0.033 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 3.5945221 Elapsed: 0.049 s\n", "\tAlgorithm: JADE f(x): 4.4548818 Elapsed: 0.034 s\n", "\tAlgorithm: jSO f(x): 1.6881663 Elapsed: 0.032 s\n", "\tAlgorithm: j2020 f(x): 6.470444 Elapsed: 0.218 s\n", "\tAlgorithm: LSRTDE f(x): 7.3764536 Elapsed: 0.031 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 4.8143538 Elapsed: 0.019 s\n", "\tAlgorithm: ARRDE f(x): 0.33501158 Elapsed: 0.048 s\n", "\tAlgorithm: GWO_DE f(x): 6.5618449 Elapsed: 0.018 s\n", "\tAlgorithm: NelderMead f(x): 3.1653044e-27 Elapsed: 0.095 s\n", "\tAlgorithm: DA f(x): 1.346784e-13 Elapsed: 0.117 s\n", "\tAlgorithm: L_BFGS_B f(x): 4.307979e-13 Elapsed: 0.008 s\n", "\tAlgorithm: PSO f(x): 3.6620106 Elapsed: 0.015 s\n", "\tAlgorithm: DMSPSO f(x): 3.0666978 Elapsed: 0.019 s\n", "\tAlgorithm: SPSO2011 f(x): 8.3681959 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): 0.070751751 Elapsed: 0.035 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 2.4385611e-20 Elapsed: 0.049 s\n", "\tAlgorithm: RCMAES f(x): 2.0289928e-07 Elapsed: 0.043 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 8.9958364e-09 Elapsed: 0.071 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 1.1088291e-10 Elapsed: 0.031 s\n", "\tAlgorithm: Scipy DA f(x): 1.5264733e-10 Elapsed: 0.329 s\n", "\n", "Function: rastrigin\n", "\tAlgorithm: DE f(x): 27.858783 Elapsed: 0.016 s\n", "\tAlgorithm: ABC f(x): 1.020665 Elapsed: 0.031 s\n", "\tAlgorithm: LSHADE f(x): 0.75359441 Elapsed: 0.022 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 6.8611038 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 0 Elapsed: 0.082 s\n", "\tAlgorithm: jSO f(x): 11.979767 Elapsed: 0.017 s\n", "\tAlgorithm: j2020 f(x): 2.9848772 Elapsed: 0.230 s\n", "\tAlgorithm: LSRTDE f(x): 5.2631939 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 2.0455492 Elapsed: 0.018 s\n", "\tAlgorithm: ARRDE f(x): 5.9697544 Elapsed: 0.035 s\n", "\tAlgorithm: GWO_DE f(x): 25.399503 Elapsed: 0.016 s\n", "\tAlgorithm: NelderMead f(x): 102.47964 Elapsed: 0.032 s\n", "\tAlgorithm: DA f(x): 0 Elapsed: 0.105 s\n", "\tAlgorithm: L_BFGS_B f(x): 102.47964 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 6.9647134 Elapsed: 0.017 s\n", "\tAlgorithm: DMSPSO f(x): 4.9747953 Elapsed: 0.015 s\n", "\tAlgorithm: SPSO2011 f(x): 22.502709 Elapsed: 0.031 s\n", "\tAlgorithm: CMAES f(x): 7.9596674 Elapsed: 0.017 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 15.292944 Elapsed: 0.050 s\n", "\tAlgorithm: RCMAES f(x): 0.99495906 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 254.70399 Elapsed: 0.032 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 237.78983 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): 0.99495906 Elapsed: 0.356 s\n", "\n", "Function: schaffer2\n", "\tAlgorithm: DE f(x): 0.55808408 Elapsed: 0.228 s\n", "\tAlgorithm: ABC f(x): 0.15968907 Elapsed: 0.233 s\n", "\tAlgorithm: LSHADE f(x): 0.2075968 Elapsed: 0.250 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.19970339 Elapsed: 0.253 s\n", "\tAlgorithm: JADE f(x): 0.091651581 Elapsed: 0.257 s\n", "\tAlgorithm: jSO f(x): 0.89364912 Elapsed: 0.246 s\n", "\tAlgorithm: j2020 f(x): 0.45049547 Elapsed: 0.410 s\n", "\tAlgorithm: LSRTDE f(x): 0.55253902 Elapsed: 0.247 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.20241981 Elapsed: 0.290 s\n", "\tAlgorithm: ARRDE f(x): 0.19747585 Elapsed: 0.330 s\n", "\tAlgorithm: GWO_DE f(x): 0.25339952 Elapsed: 0.246 s\n", "\tAlgorithm: NelderMead f(x): 0.18775994 Elapsed: 0.190 s\n", "\tAlgorithm: DA f(x): 0.048579908 Elapsed: 0.284 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.18775994 Elapsed: 0.029 s\n", "\tAlgorithm: PSO f(x): 0.13286684 Elapsed: 0.249 s\n", "\tAlgorithm: DMSPSO f(x): 0.36193056 Elapsed: 0.249 s\n", "\tAlgorithm: SPSO2011 f(x): 0.40766972 Elapsed: 0.250 s\n", "\tAlgorithm: CMAES f(x): 0.44386077 Elapsed: 0.249 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.31475046 Elapsed: 0.252 s\n", "\tAlgorithm: RCMAES f(x): 0.22498402 Elapsed: 0.266 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 0.38943872 Elapsed: 0.063 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.38943873 Elapsed: 0.047 s\n", "\tAlgorithm: Scipy DA f(x): 0.068011444 Elapsed: 0.546 s\n", "\n", "Function: griewank\n", "\tAlgorithm: DE f(x): 0.061438138 Elapsed: 0.022 s\n", "\tAlgorithm: ABC f(x): 0.011294394 Elapsed: 0.025 s\n", "\tAlgorithm: LSHADE f(x): 0.020743875 Elapsed: 0.026 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.015869195 Elapsed: 0.035 s\n", "\tAlgorithm: JADE f(x): 0.0074222286 Elapsed: 0.033 s\n", "\tAlgorithm: jSO f(x): 0.04904563 Elapsed: 0.031 s\n", "\tAlgorithm: j2020 f(x): 0.0073960403 Elapsed: 0.251 s\n", "\tAlgorithm: LSRTDE f(x): 0.0098688998 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.010269096 Elapsed: 0.016 s\n", "\tAlgorithm: ARRDE f(x): 0 Elapsed: 0.049 s\n", "\tAlgorithm: GWO_DE f(x): 0.039632747 Elapsed: 0.022 s\n", "\tAlgorithm: NelderMead f(x): 0.02461003 Elapsed: 0.030 s\n", "\tAlgorithm: DA f(x): 0.056544974 Elapsed: 0.184 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.02461003 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 0.04187073 Elapsed: 0.022 s\n", "\tAlgorithm: DMSPSO f(x): 0.07381799 Elapsed: 0.011 s\n", "\tAlgorithm: SPSO2011 f(x): 0.10315104 Elapsed: 0.017 s\n", "\tAlgorithm: CMAES f(x): 1.7674751e-13 Elapsed: 0.015 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.012320989 Elapsed: 0.048 s\n", "\tAlgorithm: RCMAES f(x): 2.5124347e-13 Elapsed: 0.053 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 0.046729411 Elapsed: 0.050 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.024637061 Elapsed: 0.017 s\n", "\tAlgorithm: Scipy DA f(x): 0.039362843 Elapsed: 0.399 s\n", "\n", "Function: ackley\n", "\tAlgorithm: DE f(x): 2.0133155 Elapsed: 0.035 s\n", "\tAlgorithm: ABC f(x): 0.0020388172 Elapsed: 0.020 s\n", "\tAlgorithm: LSHADE f(x): 7.5487711e-05 Elapsed: 0.034 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 6.8326518e-06 Elapsed: 0.027 s\n", "\tAlgorithm: JADE f(x): 7.5495166e-15 Elapsed: 0.033 s\n", "\tAlgorithm: jSO f(x): 1.7451542e-05 Elapsed: 0.031 s\n", "\tAlgorithm: j2020 f(x): 1.0177455e-05 Elapsed: 0.285 s\n", "\tAlgorithm: LSRTDE f(x): 2.0897127e-05 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.00017424934 Elapsed: 0.016 s\n", "\tAlgorithm: ARRDE f(x): 4.5763393e-12 Elapsed: 0.051 s\n", "\tAlgorithm: GWO_DE f(x): 0.0017379938 Elapsed: 0.016 s\n", "\tAlgorithm: NelderMead f(x): 9.0738777 Elapsed: 0.035 s\n", "\tAlgorithm: DA f(x): 2.3137048e-13 Elapsed: 0.150 s\n", "\tAlgorithm: L_BFGS_B f(x): 9.4529928 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 1.503355e-08 Elapsed: 0.024 s\n", "\tAlgorithm: DMSPSO f(x): 4.155741e-08 Elapsed: 0.019 s\n", "\tAlgorithm: SPSO2011 f(x): 0.00022512629 Elapsed: 0.029 s\n", "\tAlgorithm: CMAES f(x): 4.6584958e-13 Elapsed: 0.024 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 3.952394e-14 Elapsed: 0.054 s\n", "\tAlgorithm: RCMAES f(x): 5.0137672e-13 Elapsed: 0.034 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 12.719749 Elapsed: 0.036 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 12.719749 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): 1.9576966e-08 Elapsed: 0.444 s\n", "\n", "Function: zakharov\n", "\tAlgorithm: DE f(x): 3.0069894e-17 Elapsed: 0.023 s\n", "\tAlgorithm: ABC f(x): 41.659593 Elapsed: 0.018 s\n", "\tAlgorithm: LSHADE f(x): 3.2890263e-06 Elapsed: 0.028 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 2.4950129e-10 Elapsed: 0.034 s\n", "\tAlgorithm: JADE f(x): 1.390713e-10 Elapsed: 0.032 s\n", "\tAlgorithm: jSO f(x): 3.8538016e-07 Elapsed: 0.035 s\n", "\tAlgorithm: j2020 f(x): 0.0014664174 Elapsed: 0.353 s\n", "\tAlgorithm: LSRTDE f(x): 9.1337203e-09 Elapsed: 0.009 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.0015443574 Elapsed: 0.038 s\n", "\tAlgorithm: ARRDE f(x): 3.7159974e-15 Elapsed: 0.031 s\n", "\tAlgorithm: GWO_DE f(x): 0.0055891047 Elapsed: 0.032 s\n", "\tAlgorithm: NelderMead f(x): 9.5208937e-30 Elapsed: 0.092 s\n", "\tAlgorithm: DA f(x): 4.8003483e-15 Elapsed: 0.164 s\n", "\tAlgorithm: L_BFGS_B f(x): 1.1426143e-12 Elapsed: 0.008 s\n", "\tAlgorithm: PSO f(x): 1.2678103e-08 Elapsed: 0.018 s\n", "\tAlgorithm: DMSPSO f(x): 8.2620301e-09 Elapsed: 0.018 s\n", "\tAlgorithm: SPSO2011 f(x): 0.0035937021 Elapsed: 0.019 s\n", "\tAlgorithm: CMAES f(x): 2.0954112e-13 Elapsed: 0.021 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 2.6726884e-27 Elapsed: 0.060 s\n", "\tAlgorithm: RCMAES f(x): 2.1318679e-13 Elapsed: 0.038 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 7.6416259e-09 Elapsed: 0.137 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 7.1099823e-14 Elapsed: 0.019 s\n", "\tAlgorithm: Scipy DA f(x): 5.6595236e-11 Elapsed: 0.474 s\n", "\n", "Function: bent_cigar\n", "\tAlgorithm: DE f(x): 0.4565648 Elapsed: 0.017 s\n", "\tAlgorithm: ABC f(x): 0.0070557844 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE f(x): 0.0010852176 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 3.8386931e-06 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 2.5637979e-24 Elapsed: 0.046 s\n", "\tAlgorithm: jSO f(x): 0.00016247901 Elapsed: 0.021 s\n", "\tAlgorithm: j2020 f(x): 1.2573978e-09 Elapsed: 0.220 s\n", "\tAlgorithm: LSRTDE f(x): 7.1736159e-05 Elapsed: 0.024 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.049444144 Elapsed: 0.028 s\n", "\tAlgorithm: ARRDE f(x): 6.0498801e-18 Elapsed: 0.043 s\n", "\tAlgorithm: GWO_DE f(x): 0.43844519 Elapsed: 0.021 s\n", "\tAlgorithm: NelderMead f(x): 7.6172326e-24 Elapsed: 0.069 s\n", "\tAlgorithm: DA f(x): 3.9788316e-13 Elapsed: 0.155 s\n", "\tAlgorithm: L_BFGS_B f(x): 6.5127066e-14 Elapsed: 0.001 s\n", "\tAlgorithm: PSO f(x): 1.2779717e-08 Elapsed: 0.021 s\n", "\tAlgorithm: DMSPSO f(x): 1.1574732e-08 Elapsed: 0.021 s\n", "\tAlgorithm: SPSO2011 f(x): 60.436117 Elapsed: 0.095 s\n", "\tAlgorithm: CMAES f(x): 2.0527727e-05 Elapsed: 0.033 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 4.2892509e-21 Elapsed: 0.059 s\n", "\tAlgorithm: RCMAES f(x): 3.7040872e-13 Elapsed: 0.034 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 98.947303 Elapsed: 0.041 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 2.0992469e-10 Elapsed: 0.021 s\n", "\tAlgorithm: Scipy DA f(x): 2.2499054e-10 Elapsed: 0.378 s\n", "\n", "Function: levy\n", "\tAlgorithm: DE f(x): 3.0346786e-05 Elapsed: 0.034 s\n", "\tAlgorithm: ABC f(x): 4.9142506e-06 Elapsed: 0.026 s\n", "\tAlgorithm: LSHADE f(x): 1.4546568e-08 Elapsed: 0.033 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 1.2586431e-10 Elapsed: 0.046 s\n", "\tAlgorithm: JADE f(x): 5.592751e-30 Elapsed: 0.064 s\n", "\tAlgorithm: jSO f(x): 2.5195385e-10 Elapsed: 0.045 s\n", "\tAlgorithm: j2020 f(x): 3.3202314e-13 Elapsed: 0.505 s\n", "\tAlgorithm: LSRTDE f(x): 3.9050784e-12 Elapsed: 0.063 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 5.6983619e-08 Elapsed: 0.034 s\n", "\tAlgorithm: ARRDE f(x): 7.4846351e-25 Elapsed: 0.050 s\n", "\tAlgorithm: GWO_DE f(x): 1.1449741e-06 Elapsed: 0.031 s\n", "\tAlgorithm: NelderMead f(x): 2.3300009 Elapsed: 0.051 s\n", "\tAlgorithm: DA f(x): 2.5016759e-13 Elapsed: 0.266 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.089528251 Elapsed: 0.006 s\n", "\tAlgorithm: PSO f(x): 2.5763755e-14 Elapsed: 0.010 s\n", "\tAlgorithm: DMSPSO f(x): 7.1861345e-16 Elapsed: 0.033 s\n", "\tAlgorithm: SPSO2011 f(x): 1.311917e-05 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): 2.6149947e-13 Elapsed: 0.022 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 2.243583e-05 Elapsed: 0.078 s\n", "\tAlgorithm: RCMAES f(x): 3.0376402e-13 Elapsed: 0.041 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 10.612364 Elapsed: 0.116 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 5.6358232 Elapsed: 0.045 s\n", "\tAlgorithm: Scipy DA f(x): 4.2764763e-11 Elapsed: 0.569 s\n", "\n", "Function: discus\n", "\tAlgorithm: DE f(x): 5.7178715e-08 Elapsed: 0.019 s\n", "\tAlgorithm: ABC f(x): 0.0011298486 Elapsed: 0.016 s\n", "\tAlgorithm: LSHADE f(x): 2.7438801e-09 Elapsed: 0.028 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 3.032465e-10 Elapsed: 0.034 s\n", "\tAlgorithm: JADE f(x): 1.9721523e-31 Elapsed: 0.030 s\n", "\tAlgorithm: jSO f(x): 5.2950688e-11 Elapsed: 0.025 s\n", "\tAlgorithm: j2020 f(x): 3.0727928e-13 Elapsed: 0.205 s\n", "\tAlgorithm: LSRTDE f(x): 1.2661138e-11 Elapsed: 0.018 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 3.820779e-07 Elapsed: 0.016 s\n", "\tAlgorithm: ARRDE f(x): 1.7299206e-24 Elapsed: 0.035 s\n", "\tAlgorithm: GWO_DE f(x): 7.2145799e-05 Elapsed: 0.016 s\n", "\tAlgorithm: NelderMead f(x): 2.5838254e-24 Elapsed: 0.100 s\n", "\tAlgorithm: DA f(x): 3.6743993e-14 Elapsed: 0.133 s\n", "\tAlgorithm: L_BFGS_B f(x): 1.2333528e-13 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 1.9777901e-11 Elapsed: 0.016 s\n", "\tAlgorithm: DMSPSO f(x): 3.0682375e-13 Elapsed: 0.019 s\n", "\tAlgorithm: SPSO2011 f(x): 55.988271 Elapsed: 0.025 s\n", "\tAlgorithm: CMAES f(x): 3.0144923e-13 Elapsed: 0.022 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 5.3619245e-15 Elapsed: 0.054 s\n", "\tAlgorithm: RCMAES f(x): 2.1869038e-13 Elapsed: 0.031 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 7.8818509e-09 Elapsed: 0.164 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 7.8454388e-12 Elapsed: 0.021 s\n", "\tAlgorithm: Scipy DA f(x): 8.3484436e-10 Elapsed: 0.313 s\n", "\n", "Function: drop_wave\n", "\tAlgorithm: DE f(x): -0.93624533 Elapsed: 0.016 s\n", "\tAlgorithm: ABC f(x): -0.93624524 Elapsed: 0.037 s\n", "\tAlgorithm: LSHADE f(x): -1 Elapsed: 0.049 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): -1 Elapsed: 0.052 s\n", "\tAlgorithm: JADE f(x): -1 Elapsed: 0.034 s\n", "\tAlgorithm: jSO f(x): -1 Elapsed: 0.050 s\n", "\tAlgorithm: j2020 f(x): -1 Elapsed: 0.164 s\n", "\tAlgorithm: LSRTDE f(x): -1 Elapsed: 0.033 s\n", "\tAlgorithm: NLSHADE_RSP f(x): -1 Elapsed: 0.056 s\n", "\tAlgorithm: ARRDE f(x): -1 Elapsed: 0.047 s\n", "\tAlgorithm: GWO_DE f(x): -1 Elapsed: 0.050 s\n", "\tAlgorithm: NelderMead f(x): -1 Elapsed: 0.016 s\n", "\tAlgorithm: DA f(x): -0.93624533 Elapsed: 0.100 s\n", "\tAlgorithm: L_BFGS_B f(x): -0.93624533 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): -1 Elapsed: 0.050 s\n", "\tAlgorithm: DMSPSO f(x): -1 Elapsed: 0.032 s\n", "\tAlgorithm: SPSO2011 f(x): -1 Elapsed: 0.050 s\n", "\tAlgorithm: CMAES f(x): -0.98989002 Elapsed: 0.058 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): -0.98536311 Elapsed: 0.057 s\n", "\tAlgorithm: RCMAES f(x): -0.99953976 Elapsed: 0.054 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): -0.053939199 Elapsed: 0.016 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): -0.060920875 Elapsed: 0.015 s\n", "\tAlgorithm: Scipy DA f(x): -1 Elapsed: 0.350 s\n", "\n", "Function: goldstein_price\n", "\tAlgorithm: DE f(x): 3 Elapsed: 0.017 s\n", "\tAlgorithm: ABC f(x): 31.223228 Elapsed: 0.033 s\n", "\tAlgorithm: LSHADE f(x): 3 Elapsed: 0.049 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 3 Elapsed: 0.050 s\n", "\tAlgorithm: JADE f(x): 3 Elapsed: 0.040 s\n", "\tAlgorithm: jSO f(x): 3 Elapsed: 0.049 s\n", "\tAlgorithm: j2020 f(x): 3 Elapsed: 0.182 s\n", "\tAlgorithm: LSRTDE f(x): 3 Elapsed: 0.033 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 3 Elapsed: 0.050 s\n", "\tAlgorithm: ARRDE f(x): 3 Elapsed: 0.064 s\n", "\tAlgorithm: GWO_DE f(x): 3 Elapsed: 0.034 s\n", "\tAlgorithm: NelderMead f(x): 3 Elapsed: 0.016 s\n", "\tAlgorithm: DA f(x): 3 Elapsed: 0.082 s\n", "\tAlgorithm: L_BFGS_B f(x): 3 Elapsed: 0.016 s\n", "\tAlgorithm: PSO f(x): 3 Elapsed: 0.032 s\n", "\tAlgorithm: DMSPSO f(x): 3 Elapsed: 0.032 s\n", "\tAlgorithm: SPSO2011 f(x): 3.000006 Elapsed: 0.032 s\n", "\tAlgorithm: CMAES f(x): 3 Elapsed: 0.048 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 3 Elapsed: 0.057 s\n", "\tAlgorithm: RCMAES f(x): 3 Elapsed: 0.048 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 84 Elapsed: 0.019 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 3 Elapsed: 0.016 s\n", "\tAlgorithm: Scipy DA f(x): 3 Elapsed: 0.302 s\n", "\n", "Function: exponential\n", "\tAlgorithm: DE f(x): -0.9999973 Elapsed: 0.014 s\n", "\tAlgorithm: ABC f(x): -0.99999745 Elapsed: 0.019 s\n", "\tAlgorithm: LSHADE f(x): -1 Elapsed: 0.032 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): -1 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): -1 Elapsed: 0.016 s\n", "\tAlgorithm: jSO f(x): -1 Elapsed: 0.033 s\n", "\tAlgorithm: j2020 f(x): -1 Elapsed: 0.200 s\n", "\tAlgorithm: LSRTDE f(x): -1 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): -0.99999995 Elapsed: 0.024 s\n", "\tAlgorithm: ARRDE f(x): -1 Elapsed: 0.042 s\n", "\tAlgorithm: GWO_DE f(x): -0.99999797 Elapsed: 0.016 s\n", "\tAlgorithm: NelderMead f(x): -1 Elapsed: 0.033 s\n", "\tAlgorithm: DA f(x): -6.8852514e-10 Elapsed: 0.117 s\n", "\tAlgorithm: L_BFGS_B f(x): -1.609051e-22 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): -1 Elapsed: 0.016 s\n", "\tAlgorithm: DMSPSO f(x): -1 Elapsed: 0.016 s\n", "\tAlgorithm: SPSO2011 f(x): -0.99999999 Elapsed: 0.016 s\n", "\tAlgorithm: CMAES f(x): -8.0949067e-19 Elapsed: 0.000 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): -1 Elapsed: 0.048 s\n", "\tAlgorithm: RCMAES f(x): -1.4722505e-13 Elapsed: 0.103 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): -1 Elapsed: 0.053 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): -5.2446221e-54 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): -8.0337727e-17 Elapsed: 0.359 s\n", "\n", "Function: quartic\n", "\tAlgorithm: DE f(x): 0.00669347 Elapsed: 0.021 s\n", "\tAlgorithm: ABC f(x): 0.050228366 Elapsed: 0.019 s\n", "\tAlgorithm: LSHADE f(x): 0.013966561 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.010834201 Elapsed: 0.032 s\n", "\tAlgorithm: JADE f(x): 0.003113222 Elapsed: 0.050 s\n", "\tAlgorithm: jSO f(x): 0.0077145181 Elapsed: 0.017 s\n", "\tAlgorithm: j2020 f(x): 0.018412036 Elapsed: 0.249 s\n", "\tAlgorithm: LSRTDE f(x): 0.010920141 Elapsed: 0.033 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.01400187 Elapsed: 0.017 s\n", "\tAlgorithm: ARRDE f(x): 0.0023344029 Elapsed: 0.034 s\n", "\tAlgorithm: GWO_DE f(x): 0.011634361 Elapsed: 0.033 s\n", "\tAlgorithm: NelderMead f(x): 2.439906 Elapsed: 0.099 s\n", "\tAlgorithm: DA f(x): 0.0019139852 Elapsed: 0.117 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.13734697 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 0.0051740768 Elapsed: 0.083 s\n", "\tAlgorithm: DMSPSO f(x): 0.003703311 Elapsed: 0.033 s\n", "\tAlgorithm: SPSO2011 f(x): 0.014348103 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): 0.0071509288 Elapsed: 0.000 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.0015759651 Elapsed: 0.067 s\n", "\tAlgorithm: RCMAES f(x): 0.0035346978 Elapsed: 0.051 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 6.1571452 Elapsed: 0.300 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 50784.069 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): 0.23549417 Elapsed: 0.383 s\n", "\n", "Function: hybrid_composition1\n", "\tAlgorithm: DE f(x): 8.9988431 Elapsed: 0.033 s\n", "\tAlgorithm: ABC f(x): 20.357668 Elapsed: 0.035 s\n", "\tAlgorithm: LSHADE f(x): 8.9683112 Elapsed: 0.032 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 8.9683085 Elapsed: 0.050 s\n", "\tAlgorithm: JADE f(x): 8.9683084 Elapsed: 0.049 s\n", "\tAlgorithm: jSO f(x): 8.9683085 Elapsed: 0.033 s\n", "\tAlgorithm: j2020 f(x): 50.161162 Elapsed: 0.366 s\n", "\tAlgorithm: LSRTDE f(x): 8.9683084 Elapsed: 0.018 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 8.9683171 Elapsed: 0.033 s\n", "\tAlgorithm: ARRDE f(x): 8.9683084 Elapsed: 0.050 s\n", "\tAlgorithm: GWO_DE f(x): 8.9714542 Elapsed: 0.033 s\n", "\tAlgorithm: NelderMead f(x): 8.9683084 Elapsed: 0.067 s\n", "\tAlgorithm: DA f(x): 8.9683084 Elapsed: 0.183 s\n", "\tAlgorithm: L_BFGS_B f(x): 129.896 Elapsed: 0.007 s\n", "\tAlgorithm: PSO f(x): 43.165578 Elapsed: 0.010 s\n", "\tAlgorithm: DMSPSO f(x): 8.9683084 Elapsed: 0.033 s\n", "\tAlgorithm: SPSO2011 f(x): 8.9683112 Elapsed: 0.028 s\n", "\tAlgorithm: CMAES f(x): 8.9683084 Elapsed: 0.035 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 8.9683084 Elapsed: 0.064 s\n", "\tAlgorithm: RCMAES f(x): 8.9683085 Elapsed: 0.039 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 81.481178 Elapsed: 0.060 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 49.451818 Elapsed: 0.016 s\n", "\tAlgorithm: Scipy DA f(x): 127.3888 Elapsed: 0.539 s\n", "\n", "Function: hybrid_composition2\n", "\tAlgorithm: DE f(x): 0.00065631029 Elapsed: 0.036 s\n", "\tAlgorithm: ABC f(x): 0.0014850221 Elapsed: 0.029 s\n", "\tAlgorithm: LSHADE f(x): 7.928088e-05 Elapsed: 0.040 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 8.4663499e-05 Elapsed: 0.044 s\n", "\tAlgorithm: JADE f(x): 8.437695e-15 Elapsed: 0.048 s\n", "\tAlgorithm: jSO f(x): 3.9360297e-05 Elapsed: 0.037 s\n", "\tAlgorithm: j2020 f(x): 9.4931414e-09 Elapsed: 0.511 s\n", "\tAlgorithm: LSRTDE f(x): 1.8285006e-05 Elapsed: 0.032 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 2.7129552e-05 Elapsed: 0.032 s\n", "\tAlgorithm: ARRDE f(x): 1.1080691e-12 Elapsed: 0.064 s\n", "\tAlgorithm: GWO_DE f(x): 0.0064449574 Elapsed: 0.028 s\n", "\tAlgorithm: NelderMead f(x): 2.1270735 Elapsed: 0.181 s\n", "\tAlgorithm: DA f(x): 7.1784852e-13 Elapsed: 0.257 s\n", "\tAlgorithm: L_BFGS_B f(x): 3.4506903e-11 Elapsed: 0.014 s\n", "\tAlgorithm: PSO f(x): 4.7867762e-07 Elapsed: 0.026 s\n", "\tAlgorithm: DMSPSO f(x): 1.1121672e-07 Elapsed: 0.028 s\n", "\tAlgorithm: SPSO2011 f(x): 0.00071767757 Elapsed: 0.038 s\n", "\tAlgorithm: CMAES f(x): 4.7674408e-13 Elapsed: 0.040 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 3.1845533e-13 Elapsed: 0.080 s\n", "\tAlgorithm: RCMAES f(x): 5.9952043e-13 Elapsed: 0.051 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 9.7571206 Elapsed: 0.187 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 5.4366301e-08 Elapsed: 0.114 s\n", "\tAlgorithm: Scipy DA f(x): 6.7994238e-08 Elapsed: 0.732 s\n", "\n", "Function: hybrid_composition3\n", "\tAlgorithm: DE f(x): 1.2839526 Elapsed: 0.040 s\n", "\tAlgorithm: ABC f(x): 27.704459 Elapsed: 0.055 s\n", "\tAlgorithm: LSHADE f(x): 1.2839527 Elapsed: 0.053 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 1.2839528 Elapsed: 0.061 s\n", "\tAlgorithm: JADE f(x): 1.2839526 Elapsed: 0.086 s\n", "\tAlgorithm: jSO f(x): 1.2839527 Elapsed: 0.048 s\n", "\tAlgorithm: j2020 f(x): 1.2843391 Elapsed: 0.860 s\n", "\tAlgorithm: LSRTDE f(x): 1.2839526 Elapsed: 0.038 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 1.3209794 Elapsed: 0.038 s\n", "\tAlgorithm: ARRDE f(x): 1.2839526 Elapsed: 0.084 s\n", "\tAlgorithm: GWO_DE f(x): 1.2886312 Elapsed: 0.033 s\n", "\tAlgorithm: NelderMead f(x): 34.881183 Elapsed: 0.184 s\n", "\tAlgorithm: DA f(x): 1.7576298 Elapsed: 0.298 s\n", "\tAlgorithm: L_BFGS_B f(x): 2.6486818 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 1.2839526 Elapsed: 0.034 s\n", "\tAlgorithm: DMSPSO f(x): 1.2839526 Elapsed: 0.032 s\n", "\tAlgorithm: SPSO2011 f(x): 1.2905009 Elapsed: 0.049 s\n", "\tAlgorithm: CMAES f(x): 1.2839526 Elapsed: 0.051 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 1.2839526 Elapsed: 0.103 s\n", "\tAlgorithm: RCMAES f(x): 1.2839526 Elapsed: 0.089 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 95.146651 Elapsed: 0.444 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 7.1707073 Elapsed: 0.132 s\n", "\tAlgorithm: Scipy DA f(x): 86.827716 Elapsed: 0.989 s\n", "\n", "Function: happy_cat\n", "\tAlgorithm: DE f(x): 0.43438983 Elapsed: 0.036 s\n", "\tAlgorithm: ABC f(x): 0.11614202 Elapsed: 0.024 s\n", "\tAlgorithm: LSHADE f(x): 0.06876798 Elapsed: 0.032 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.08811846 Elapsed: 0.048 s\n", "\tAlgorithm: JADE f(x): 0.057854724 Elapsed: 0.051 s\n", "\tAlgorithm: jSO f(x): 0.1398287 Elapsed: 0.018 s\n", "\tAlgorithm: j2020 f(x): 0.32344314 Elapsed: 0.303 s\n", "\tAlgorithm: LSRTDE f(x): 0.067310395 Elapsed: 0.031 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.22108284 Elapsed: 0.033 s\n", "\tAlgorithm: ARRDE f(x): 0.03932664 Elapsed: 0.032 s\n", "\tAlgorithm: GWO_DE f(x): 0.092721777 Elapsed: 0.035 s\n", "\tAlgorithm: NelderMead f(x): 0.67618088 Elapsed: 0.048 s\n", "\tAlgorithm: DA f(x): 0.29259162 Elapsed: 0.088 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.48739033 Elapsed: 0.008 s\n", "\tAlgorithm: PSO f(x): 0.10764502 Elapsed: 0.016 s\n", "\tAlgorithm: DMSPSO f(x): 0.090894072 Elapsed: 0.018 s\n", "\tAlgorithm: SPSO2011 f(x): 0.11049381 Elapsed: 0.023 s\n", "\tAlgorithm: CMAES f(x): 0.079812377 Elapsed: 0.032 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.11685272 Elapsed: 0.067 s\n", "\tAlgorithm: RCMAES f(x): 0.031395191 Elapsed: 0.032 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 1.4407279 Elapsed: 0.060 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.0059492927 Elapsed: 0.038 s\n", "\tAlgorithm: Scipy DA f(x): 0.049035597 Elapsed: 0.445 s\n", "\n", "Function: michalewics\n", "\tAlgorithm: DE f(x): -8.3009355 Elapsed: 0.027 s\n", "\tAlgorithm: ABC f(x): -9.1973303 Elapsed: 0.021 s\n", "\tAlgorithm: LSHADE f(x): -9.3568629 Elapsed: 0.030 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): -7.1404582 Elapsed: 0.028 s\n", "\tAlgorithm: JADE f(x): -9.8234088 Elapsed: 0.049 s\n", "\tAlgorithm: jSO f(x): -7.14665 Elapsed: 0.035 s\n", "\tAlgorithm: j2020 f(x): -9.3705526 Elapsed: 0.266 s\n", "\tAlgorithm: LSRTDE f(x): -5.9973706 Elapsed: 0.017 s\n", "\tAlgorithm: NLSHADE_RSP f(x): -9.1494271 Elapsed: 0.035 s\n", "\tAlgorithm: ARRDE f(x): -9.1035186 Elapsed: 0.051 s\n", "\tAlgorithm: GWO_DE f(x): -5.8236661 Elapsed: 0.031 s\n", "\tAlgorithm: NelderMead f(x): -3.6164748 Elapsed: 0.181 s\n", "\tAlgorithm: DA f(x): -9.6983809 Elapsed: 0.101 s\n", "\tAlgorithm: L_BFGS_B f(x): -2.6919874 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): -8.4025835 Elapsed: 0.019 s\n", "\tAlgorithm: DMSPSO f(x): -9.2155994 Elapsed: 0.031 s\n", "\tAlgorithm: SPSO2011 f(x): -4.3098749 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): -7.593261 Elapsed: 0.041 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): -7.1249446 Elapsed: 0.076 s\n", "\tAlgorithm: RCMAES f(x): -9.12015 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): -4.2489914 Elapsed: 0.050 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): -3.2901102 Elapsed: 0.036 s\n", "\tAlgorithm: Scipy DA f(x): -9.8596763 Elapsed: 0.451 s\n", "\n", "Function: scaffer6\n", "\tAlgorithm: DE f(x): 0.39870787 Elapsed: 0.247 s\n", "\tAlgorithm: ABC f(x): 0.10500073 Elapsed: 0.238 s\n", "\tAlgorithm: LSHADE f(x): 0.16383664 Elapsed: 0.324 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.31007305 Elapsed: 0.272 s\n", "\tAlgorithm: JADE f(x): 0.096623768 Elapsed: 0.264 s\n", "\tAlgorithm: jSO f(x): 0.49340471 Elapsed: 0.267 s\n", "\tAlgorithm: j2020 f(x): 0.54162424 Elapsed: 0.434 s\n", "\tAlgorithm: LSRTDE f(x): 0.84925214 Elapsed: 0.282 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.2412924 Elapsed: 0.300 s\n", "\tAlgorithm: ARRDE f(x): 0.22498414 Elapsed: 0.267 s\n", "\tAlgorithm: GWO_DE f(x): 0.20916532 Elapsed: 0.334 s\n", "\tAlgorithm: NelderMead f(x): 0.18775994 Elapsed: 0.215 s\n", "\tAlgorithm: DA f(x): 0.08744319 Elapsed: 0.317 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.18775994 Elapsed: 0.032 s\n", "\tAlgorithm: PSO f(x): 0.14246096 Elapsed: 0.320 s\n", "\tAlgorithm: DMSPSO f(x): 0.28000035 Elapsed: 0.347 s\n", "\tAlgorithm: SPSO2011 f(x): 0.44903672 Elapsed: 0.300 s\n", "\tAlgorithm: CMAES f(x): 0.18775997 Elapsed: 0.249 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.48261702 Elapsed: 0.302 s\n", "\tAlgorithm: RCMAES f(x): 0.36193056 Elapsed: 0.297 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 0.38943872 Elapsed: 0.086 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.38943873 Elapsed: 0.044 s\n", "\tAlgorithm: Scipy DA f(x): 0.08744319 Elapsed: 0.550 s\n", "\n", "Function: hcf\n", "\tAlgorithm: DE f(x): 0.00012559037 Elapsed: 0.019 s\n", "\tAlgorithm: ABC f(x): 0.00064405979 Elapsed: 0.016 s\n", "\tAlgorithm: LSHADE f(x): 0.00026109603 Elapsed: 0.018 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 5.940098e-06 Elapsed: 0.042 s\n", "\tAlgorithm: JADE f(x): 0 Elapsed: 0.025 s\n", "\tAlgorithm: jSO f(x): 1.5504817e-05 Elapsed: 0.032 s\n", "\tAlgorithm: j2020 f(x): 1.3515534e-09 Elapsed: 0.244 s\n", "\tAlgorithm: LSRTDE f(x): 6.2804581e-06 Elapsed: 0.010 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 2.0882744e-05 Elapsed: 0.030 s\n", "\tAlgorithm: ARRDE f(x): 1.8728075e-13 Elapsed: 0.034 s\n", "\tAlgorithm: GWO_DE f(x): 0.0028549358 Elapsed: 0.030 s\n", "\tAlgorithm: NelderMead f(x): 2.358758 Elapsed: 0.170 s\n", "\tAlgorithm: DA f(x): 6.959574e-13 Elapsed: 0.083 s\n", "\tAlgorithm: L_BFGS_B f(x): 1.2448024e-11 Elapsed: 0.020 s\n", "\tAlgorithm: PSO f(x): 3.625667e-07 Elapsed: 0.014 s\n", "\tAlgorithm: DMSPSO f(x): 4.1352657e-08 Elapsed: 0.016 s\n", "\tAlgorithm: SPSO2011 f(x): 0.0008370529 Elapsed: 0.017 s\n", "\tAlgorithm: CMAES f(x): 5.6695078e-13 Elapsed: 0.017 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 3.4508637e-14 Elapsed: 0.055 s\n", "\tAlgorithm: RCMAES f(x): 6.2305716e-13 Elapsed: 0.028 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 9.2898681 Elapsed: 0.117 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 6.0597252e-08 Elapsed: 0.066 s\n", "\tAlgorithm: Scipy DA f(x): 3.908471e-08 Elapsed: 0.350 s\n", "\n", "Function: grie_rosen\n", "\tAlgorithm: DE f(x): 8.8831392 Elapsed: 0.032 s\n", "\tAlgorithm: ABC f(x): 1.7608068 Elapsed: 0.018 s\n", "\tAlgorithm: LSHADE f(x): 5.6373159 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 2.4061948 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 3.0199724 Elapsed: 0.050 s\n", "\tAlgorithm: jSO f(x): 4.0031942 Elapsed: 0.016 s\n", "\tAlgorithm: j2020 f(x): 6.1732822 Elapsed: 0.267 s\n", "\tAlgorithm: LSRTDE f(x): 6.731517 Elapsed: 0.016 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 5.5444564 Elapsed: 0.033 s\n", "\tAlgorithm: ARRDE f(x): 1.0276919 Elapsed: 0.045 s\n", "\tAlgorithm: GWO_DE f(x): 8.3330569 Elapsed: 0.022 s\n", "\tAlgorithm: NelderMead f(x): 1 Elapsed: 0.100 s\n", "\tAlgorithm: DA f(x): 1 Elapsed: 0.150 s\n", "\tAlgorithm: L_BFGS_B f(x): 1 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 5.1698908 Elapsed: 0.033 s\n", "\tAlgorithm: DMSPSO f(x): 4.1950515 Elapsed: 0.017 s\n", "\tAlgorithm: SPSO2011 f(x): 7.3833122 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): 1.0531737 Elapsed: 0.050 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 1 Elapsed: 0.067 s\n", "\tAlgorithm: RCMAES f(x): 1.0000001 Elapsed: 0.050 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 1 Elapsed: 0.116 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 1 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy DA f(x): 1 Elapsed: 0.618 s\n", "\n", "Function: dixon_price\n", "\tAlgorithm: DE f(x): 0.9369413 Elapsed: 0.032 s\n", "\tAlgorithm: ABC f(x): 0.084836119 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE f(x): 0.66667706 Elapsed: 0.033 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.66666667 Elapsed: 0.050 s\n", "\tAlgorithm: JADE f(x): 0.66666667 Elapsed: 0.033 s\n", "\tAlgorithm: jSO f(x): 0.66666667 Elapsed: 0.043 s\n", "\tAlgorithm: j2020 f(x): 1.0792427e-06 Elapsed: 0.239 s\n", "\tAlgorithm: LSRTDE f(x): 0.66666667 Elapsed: 0.033 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.66666673 Elapsed: 0.023 s\n", "\tAlgorithm: ARRDE f(x): 0.011844537 Elapsed: 0.028 s\n", "\tAlgorithm: GWO_DE f(x): 0.6670176 Elapsed: 0.016 s\n", "\tAlgorithm: NelderMead f(x): 0.66666667 Elapsed: 0.053 s\n", "\tAlgorithm: DA f(x): 0.66666667 Elapsed: 0.164 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.66666667 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 0.66666667 Elapsed: 0.018 s\n", "\tAlgorithm: DMSPSO f(x): 0.66666667 Elapsed: 0.031 s\n", "\tAlgorithm: SPSO2011 f(x): 0.66871542 Elapsed: 0.017 s\n", "\tAlgorithm: CMAES f(x): 0.66666667 Elapsed: 0.017 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 1.1528975e-22 Elapsed: 0.065 s\n", "\tAlgorithm: RCMAES f(x): 0.66666667 Elapsed: 0.018 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 0.66666667 Elapsed: 0.098 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.66666667 Elapsed: 0.017 s\n", "\tAlgorithm: Scipy DA f(x): 0.66666667 Elapsed: 0.416 s\n", "\n", "Function: eosom\n", "\tAlgorithm: DE f(x): -0.0090056781 Elapsed: 0.017 s\n", "\tAlgorithm: ABC f(x): -0.0090051278 Elapsed: 0.050 s\n", "\tAlgorithm: LSHADE f(x): -0.0090056781 Elapsed: 0.050 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): -0.0090056781 Elapsed: 0.067 s\n", "\tAlgorithm: JADE f(x): -0.0090056781 Elapsed: 0.033 s\n", "\tAlgorithm: jSO f(x): -0.0090056781 Elapsed: 0.050 s\n", "\tAlgorithm: j2020 f(x): -0.0090056781 Elapsed: 0.183 s\n", "\tAlgorithm: LSRTDE f(x): -0.0090056781 Elapsed: 0.050 s\n", "\tAlgorithm: NLSHADE_RSP f(x): -0.0090056781 Elapsed: 0.050 s\n", "\tAlgorithm: ARRDE f(x): -0.0090056781 Elapsed: 0.067 s\n", "\tAlgorithm: GWO_DE f(x): -0.0090056781 Elapsed: 0.051 s\n", "\tAlgorithm: NelderMead f(x): -7.8737549e-14 Elapsed: 0.033 s\n", "\tAlgorithm: DA f(x): -0.0090056781 Elapsed: 0.117 s\n", "\tAlgorithm: L_BFGS_B f(x): 2.8026392e-10 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): -0.0090056781 Elapsed: 0.050 s\n", "\tAlgorithm: DMSPSO f(x): -0.0090056781 Elapsed: 0.049 s\n", "\tAlgorithm: SPSO2011 f(x): -0.0090056781 Elapsed: 0.067 s\n", "\tAlgorithm: CMAES f(x): -0.0090056781 Elapsed: 0.075 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): -0.0090056781 Elapsed: 0.074 s\n", "\tAlgorithm: RCMAES f(x): -0.0090056781 Elapsed: 0.084 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): -0.0090056781 Elapsed: 0.019 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): -1.4567312e-07 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): -0.0090056781 Elapsed: 0.301 s\n", "\n", "Function: hgbat\n", "\tAlgorithm: DE f(x): 0.50187639 Elapsed: 0.031 s\n", "\tAlgorithm: ABC f(x): 0.51336039 Elapsed: 0.015 s\n", "\tAlgorithm: LSHADE f(x): 0.50006438 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0.50001148 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 0.5 Elapsed: 0.050 s\n", "\tAlgorithm: jSO f(x): 0.50000443 Elapsed: 0.017 s\n", "\tAlgorithm: j2020 f(x): 0.50000019 Elapsed: 0.285 s\n", "\tAlgorithm: LSRTDE f(x): 0.50002916 Elapsed: 0.015 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0.50039586 Elapsed: 0.033 s\n", "\tAlgorithm: ARRDE f(x): 0.5 Elapsed: 0.036 s\n", "\tAlgorithm: GWO_DE f(x): 0.50185635 Elapsed: 0.030 s\n", "\tAlgorithm: NelderMead f(x): 0.5 Elapsed: 0.070 s\n", "\tAlgorithm: DA f(x): 0.50000001 Elapsed: 0.114 s\n", "\tAlgorithm: L_BFGS_B f(x): 0.50000001 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 0.50000007 Elapsed: 0.018 s\n", "\tAlgorithm: DMSPSO f(x): 0.50000007 Elapsed: 0.020 s\n", "\tAlgorithm: SPSO2011 f(x): 0.50010564 Elapsed: 0.013 s\n", "\tAlgorithm: CMAES f(x): 0.5 Elapsed: 0.036 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.5 Elapsed: 0.064 s\n", "\tAlgorithm: RCMAES f(x): 0.5 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 0.5000703 Elapsed: 0.049 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 0.50000002 Elapsed: 0.034 s\n", "\tAlgorithm: Scipy DA f(x): 0.50000001 Elapsed: 0.383 s\n", "\n", "Function: styblinski_tang\n", "\tAlgorithm: DE f(x): -363.38784 Elapsed: 0.018 s\n", "\tAlgorithm: ABC f(x): -391.66152 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE f(x): -391.66076 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): -391.66165 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): -377.52494 Elapsed: 0.034 s\n", "\tAlgorithm: jSO f(x): -391.66166 Elapsed: 0.017 s\n", "\tAlgorithm: j2020 f(x): -391.62532 Elapsed: 0.216 s\n", "\tAlgorithm: LSRTDE f(x): -391.66166 Elapsed: 0.017 s\n", "\tAlgorithm: NLSHADE_RSP f(x): -391.66122 Elapsed: 0.033 s\n", "\tAlgorithm: ARRDE f(x): -391.66166 Elapsed: 0.033 s\n", "\tAlgorithm: GWO_DE f(x): -377.52492 Elapsed: 0.017 s\n", "\tAlgorithm: NelderMead f(x): -306.84134 Elapsed: 0.048 s\n", "\tAlgorithm: DA f(x): -391.66166 Elapsed: 0.125 s\n", "\tAlgorithm: L_BFGS_B f(x): -320.97806 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): -363.38822 Elapsed: 0.110 s\n", "\tAlgorithm: DMSPSO f(x): -377.52494 Elapsed: 0.016 s\n", "\tAlgorithm: SPSO2011 f(x): -377.52492 Elapsed: 0.033 s\n", "\tAlgorithm: CMAES f(x): -377.52494 Elapsed: 0.017 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): -349.2515 Elapsed: 0.050 s\n", "\tAlgorithm: RCMAES f(x): -377.52494 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): -250.29447 Elapsed: 0.050 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): -292.70462 Elapsed: 0.016 s\n", "\tAlgorithm: Scipy DA f(x): -391.66166 Elapsed: 0.350 s\n", "\n", "Function: step\n", "\tAlgorithm: DE f(x): 1 Elapsed: 0.000 s\n", "\tAlgorithm: ABC f(x): 0 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE f(x): 0 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 0 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 0 Elapsed: 0.034 s\n", "\tAlgorithm: jSO f(x): 0 Elapsed: 0.025 s\n", "\tAlgorithm: j2020 f(x): 0 Elapsed: 0.223 s\n", "\tAlgorithm: LSRTDE f(x): 0 Elapsed: 0.017 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 0 Elapsed: 0.023 s\n", "\tAlgorithm: ARRDE f(x): 0 Elapsed: 0.035 s\n", "\tAlgorithm: GWO_DE f(x): 0 Elapsed: 0.022 s\n", "\tAlgorithm: NelderMead f(x): 98 Elapsed: 0.001 s\n", "\tAlgorithm: DA f(x): 0 Elapsed: 0.125 s\n", "\tAlgorithm: L_BFGS_B f(x): 103 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 0 Elapsed: 0.017 s\n", "\tAlgorithm: DMSPSO f(x): 0 Elapsed: 0.029 s\n", "\tAlgorithm: SPSO2011 f(x): 0 Elapsed: 0.027 s\n", "\tAlgorithm: CMAES f(x): 0 Elapsed: 0.004 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0 Elapsed: 0.057 s\n", "\tAlgorithm: RCMAES f(x): 0 Elapsed: 0.033 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 256 Elapsed: 0.015 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 256 Elapsed: 0.001 s\n", "\tAlgorithm: Scipy DA f(x): 0 Elapsed: 0.326 s\n", "\n", "Function: weierstrass\n", "\tAlgorithm: DE f(x): 6.6762411 Elapsed: 1.106 s\n", "\tAlgorithm: ABC f(x): 1.7689882 Elapsed: 1.120 s\n", "\tAlgorithm: LSHADE f(x): 1.843618 Elapsed: 1.036 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 3.7636512 Elapsed: 1.042 s\n", "\tAlgorithm: JADE f(x): 0.25402247 Elapsed: 1.157 s\n", "\tAlgorithm: jSO f(x): 7.1518865 Elapsed: 1.000 s\n", "\tAlgorithm: j2020 f(x): 2.6121347 Elapsed: 1.123 s\n", "\tAlgorithm: LSRTDE f(x): 10.424692 Elapsed: 1.011 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 1.5618487 Elapsed: 1.070 s\n", "\tAlgorithm: ARRDE f(x): 2.8028555 Elapsed: 1.027 s\n", "\tAlgorithm: GWO_DE f(x): 8.296778 Elapsed: 1.037 s\n", "\tAlgorithm: NelderMead f(x): 16.331325 Elapsed: 0.199 s\n", "\tAlgorithm: DA f(x): 12.581938 Elapsed: 1.020 s\n", "\tAlgorithm: L_BFGS_B f(x): 17.505271 Elapsed: 0.145 s\n", "\tAlgorithm: PSO f(x): 0.69471505 Elapsed: 1.015 s\n", "\tAlgorithm: DMSPSO f(x): 6.3828497 Elapsed: 1.017 s\n", "\tAlgorithm: SPSO2011 f(x): 9.3314811 Elapsed: 1.000 s\n", "\tAlgorithm: CMAES f(x): 9.8367704 Elapsed: 1.009 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 0.80966954 Elapsed: 1.075 s\n", "\tAlgorithm: RCMAES f(x): 10.917941 Elapsed: 1.066 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 6.4472257 Elapsed: 0.167 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 15.152619 Elapsed: 0.150 s\n", "\tAlgorithm: Scipy DA f(x): 7.8145027 Elapsed: 1.233 s\n", "\n", "Function: sum_squares\n", "\tAlgorithm: DE f(x): 7.8443974e-12 Elapsed: 0.017 s\n", "\tAlgorithm: ABC f(x): 1.1320746e-06 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE f(x): 5.0250021e-08 Elapsed: 0.017 s\n", "\tAlgorithm: LSHADE_cnEpSin f(x): 5.8031739e-11 Elapsed: 0.033 s\n", "\tAlgorithm: JADE f(x): 1.5752115e-27 Elapsed: 0.033 s\n", "\tAlgorithm: jSO f(x): 8.2499053e-11 Elapsed: 0.033 s\n", "\tAlgorithm: j2020 f(x): 8.5076449e-13 Elapsed: 0.183 s\n", "\tAlgorithm: LSRTDE f(x): 1.0575001e-10 Elapsed: 0.017 s\n", "\tAlgorithm: NLSHADE_RSP f(x): 6.9326457e-08 Elapsed: 0.017 s\n", "\tAlgorithm: ARRDE f(x): 2.4040217e-24 Elapsed: 0.033 s\n", "\tAlgorithm: GWO_DE f(x): 9.2745848e-06 Elapsed: 0.017 s\n", "\tAlgorithm: NelderMead f(x): 8.310147e-30 Elapsed: 0.050 s\n", "\tAlgorithm: DA f(x): 8.1379826e-15 Elapsed: 0.117 s\n", "\tAlgorithm: L_BFGS_B f(x): 4.0453218e-14 Elapsed: 0.000 s\n", "\tAlgorithm: PSO f(x): 6.0895571e-15 Elapsed: 0.017 s\n", "\tAlgorithm: DMSPSO f(x): 2.7514762e-15 Elapsed: 0.016 s\n", "\tAlgorithm: SPSO2011 f(x): 7.3714663e-06 Elapsed: 0.026 s\n", "\tAlgorithm: CMAES f(x): 3.4587095e-13 Elapsed: 0.008 s\n", "\tAlgorithm: BIPOP_aCMAES f(x): 2.1153437e-25 Elapsed: 0.050 s\n", "\tAlgorithm: RCMAES f(x): 3.274528e-13 Elapsed: 0.017 s\n", "\tAlgorithm: Scipy Nelder-Mead f(x): 1.6899228e-08 Elapsed: 0.050 s\n", "\tAlgorithm: Scipy L-BFGS-B f(x): 1.8569868e-11 Elapsed: 0.000 s\n", "\tAlgorithm: Scipy DA f(x): 1.8569868e-11 Elapsed: 0.349 s\n", "\n" ] } ], "source": [ "dimension = 10\n", "maxevals = 10000\n", "np.random.seed(1)\n", "shift = 10 * np.random.rand(dimension) # Shift randomly\n", "bounds = [(-10, 10)] * dimension\n", "x0 = [10 * np.random.rand(dimension)] # Initial guesses\n", "np.random.seed(None)\n", "\n", "for func_name in testFunctionDict:\n", " #if func_name not in [\"schaffer2\", \"ackley\", \"rastrigin\", \"rosenbrock\"]:\n", " # continue # Minimize only these functions; comment this line to compare all test functions\n", "\n", " print(f\"Function: {func_name}\")\n", "\n", " N = 0\n", "\n", " def objective_function(X):\n", " \"\"\"Shifted objective function for minimization.\"\"\"\n", " global N\n", " X = np.array(X) - shift # Shift the space\n", " N += len(X)\n", " return testFunctionDict[func_name](X)\n", "\n", " def objective_function_scipy(X):\n", " \"\"\"Objective function for SciPy optimizers.\"\"\"\n", " global N\n", " N += 1\n", " return testFunctionDict[func_name]([X])[0]\n", "\n", " algoList = [\n", " \"DE\", \"ABC\", \"LSHADE\", \"LSHADE_cnEpSin\", \"JADE\", \"jSO\", \"j2020\", \"LSRTDE\", \"NLSHADE_RSP\",\n", " \"ARRDE\", \"GWO_DE\", \"NelderMead\", \"DA\", \"L_BFGS_B\", \"PSO\", \"DMSPSO\", \"SPSO2011\", \"CMAES\", \"BIPOP_aCMAES\", \"RCMAES\"\n", " ]\n", " \n", "\n", " for algo in algoList:\n", " t_start = time.time()\n", " N = 0\n", " result = mpy.Minimizer(\n", " func=objective_function, x0=x0, bounds=bounds, algo=algo, \n", " relTol=0.0, maxevals=maxevals, callback=None, seed=None,\n", " options={\"population_size\": 0}\n", " ).optimize()\n", " elapsed_time = time.time() - t_start\n", " print(f\"\\tAlgorithm: {algo:<18} f(x): {result.fun:<15.8g} Elapsed: {elapsed_time:.3f} s\")\n", "\n", " # Compare to SciPy algorithms\n", " algorithms_scipy = [\n", " (\"Scipy Nelder-Mead\", minimize, {\"x0\": x0[0], \"method\": \"Nelder-Mead\", \n", " \"bounds\": bounds, \"options\": {\"maxfev\": maxevals, \"adaptive\": True}}),\n", " (\"Scipy L-BFGS-B\", minimize, {\"x0\": x0[0], \"method\": \"L-BFGS-B\", \n", " \"options\": {\"maxfun\": maxevals}, \"bounds\": bounds}),\n", " (\"Scipy DA\", dual_annealing, {\"bounds\": bounds, \"maxfun\": maxevals, \"x0\": x0[0], \"no_local_search\": False})\n", " ]\n", "\n", " for name, func, kwargs in algorithms_scipy:\n", " t_start = time.time()\n", " N = 0\n", " result = func(objective_function_scipy, **kwargs)\n", " elapsed_time = time.time() - t_start\n", " print(f\"\\tAlgorithm: {name:<18} f(x): {result.fun:<15.8g} Elapsed: {elapsed_time:.3f} s\")\n", "\n", " print(\"\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Minimizing Expensive Functions with Multithreading/Multiprocessing\n", "\n", "When the objective function is expensive to evaluate, multithreading can be used to speed up the calculation of the vectorized objective function. However, this requires that the objective function is **thread-safe**. \n", "\n", "If the objective function is not thread-safe, then **multiprocessing** can be used instead. This approach allows parallel execution across separate processes, which avoids the potential issues with thread safety.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example to vectorize a thread-safe function using multithreading and multiprocessing\n", "If the function is thread-safe to call cuncurrently, then we can safely use concurrent.futures.ThreadPoolExecutor (for multithreading) or concurrent.futures.ProcessPoolExecutor (for multiproceesing) directly. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Optimization Results:\n", "====================================================================================================\n", "Algo : ARRDE | f(x) = 117.95172 | Elapsed: 1.96 sec\n", "Algo : L_BFGS_B | f(x) = 100 | Elapsed: 1.86 sec\n", "Algo : DA | f(x) = 100 | Elapsed: 5.06 sec\n", "Algo : Scipy Dual Annealing | f(x) = 100 | Elapsed: 10.85 sec\n", "Algo : Scipy L-BFGS-B | f(x) = 100 | Elapsed: 4.64 sec\n", "====================================================================================================\n" ] } ], "source": [ "# Function to minimize (expensive to evaluate)\n", "def func(x):\n", " ret = rosenbrock(x)\n", " time.sleep(0.01) # Simulate expensive computation\n", " return ret\n", "\n", "# Parallel execution setup\n", "Nthreads = 8\n", "use_threads = True # Toggle between ThreadPoolExecutor and ProcessPoolExecutor\n", "\n", "if use_threads:\n", " executor = concurrent.futures.ThreadPoolExecutor(max_workers=Nthreads)\n", "else:\n", " executor = concurrent.futures.ProcessPoolExecutor(max_workers=Nthreads)\n", "\n", "def objective_function(X):\n", " return list(executor.map(func, X)) # Batch evaluation in parallel\n", "\n", "# Optimization problem settings\n", "dimension = 10\n", "maxevals = 1000\n", "x0 = [[3.0] * dimension]\n", "bounds = [(-10, 10)] * dimension\n", "\n", "# List of algorithms to test\n", "algorithms = {\n", " \"ARRDE\": {\"options\": None},\n", " \"L_BFGS_B\": {\"options\": {\"func_noise_ratio\": 0.0, \"N_points_derivative\": 1}},\n", " \"DA\": {\"options\": None}\n", "}\n", "\n", "print(\"\\nOptimization Results:\")\n", "print(\"=\" * 100)\n", "\n", "# Run optimizations using Minion\n", "for algo, settings in algorithms.items():\n", " start_time = time.time()\n", " minimizer = mpy.Minimizer(\n", " func=objective_function,\n", " x0=x0,\n", " bounds=bounds,\n", " algo=algo,\n", " relTol=0.0,\n", " maxevals=maxevals,\n", " callback=None,\n", " seed=None,\n", " options=settings[\"options\"]\n", " )\n", " result = minimizer.optimize()\n", " elapsed = time.time() - start_time\n", "\n", " print(f\"Algo : {algo:<30} | f(x) = {result.fun:<20.8g} | Elapsed: {elapsed:.2f} sec\")\n", "\n", "# Compare with SciPy optimizers (without multithreading)\n", "for algo, opt_func in [\n", " (\"Scipy Dual Annealing\", dual_annealing),\n", " (\"Scipy L-BFGS-B\", minimize)\n", "]:\n", " start_time = time.time()\n", " if algo == \"Scipy Dual Annealing\":\n", " result = opt_func(func, bounds=bounds, maxfun=maxevals, no_local_search=False, x0=x0[0])\n", " else:\n", " result = opt_func(func, x0=x0[0], method=\"L-BFGS-B\", options={\"maxfun\": maxevals}, bounds=bounds)\n", "\n", " elapsed = time.time() - start_time\n", " print(f\"Algo : {algo:<30} | f(x) = {result.fun:<20.8g} | Elapsed: {elapsed:.2f} sec\")\n", "\n", "print(\"=\" * 100)\n", "\n", "# Shutdown executor gracefully\n", "executor.shutdown()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The algorithms implemented in Minion (ARRDE, L-BFGS-B, and Dual Annealing) significantly outperform their SciPy counterparts in speed. For example, Minion's Dual Annealing is roughly 4 times as fast as the SciPy's, while its L-BFGS-B is almost three times as fast. This performance improvement stems from Minion’s efficient numerical derivative computation, which batches function evaluations—an approach that greatly benefits minion's L-BFGS-B and Dual Annealing. Note that dual annealing use L-BFGS-B for local search.\n", "\n", "------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using `minionpy.Thread_Parallel` to vectorize non-thread-safe member function\n", "\n", "In the previous example, we demonstrated how to minimize a thread-safe function. However, in real-world scenarios, the objective function is often a method of a class, and class methods are typically **not thread-safe**. In this example, we demonstrate how to minimize a **non-thread-safe function** using the `Thread_Parallel` class. This approach ensures proper parallelization even when the objective function modifies internal state, which can lead to race conditions in a multi-threaded environment.\n", "\n", "### 1. Define the Class with `objective_function`\n", "\n", "First, define a class that includes the `objective_function`. This function should accept a list of floats as input and return a single float as the output. In this example, the `objective_function` modifies an internal state, which makes it non-thread-safe. We will show how the minionpy `Thread_Parallel` class manages the parallel execution of such functions while ensuring thread isolation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class Objective : \n", " \"\"\" \n", " This illustrate a class with a non-thread-safe self.objective_function\n", " \"\"\"\n", " def __init__ (self, b) : \n", " self.A=None # There is a now class member that will be modified when self.objective_function is called.\n", " self.b = b\n", " \n", " def update_A(self, x) : \n", " self.A = self.b*np.sin(x) #modify self.A\n", " time.sleep(0.05) #simulate an expensive function\n", "\n", " def objective_function(self, x) : \n", " self.update_A(x)\n", " ret = np.sum(self.A*x) \n", " #print(x, ret)\n", " return ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Define the Thread_Parallel object" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vectorization using Thread_Parallel : \n", "\t [np.float64(0.5590277202277464), np.float64(0.8314047491898819), np.float64(0.42835223063884925), np.float64(0.3263644456888844), np.float64(0.710442560133207), np.float64(0.406870840521136), np.float64(0.5407012538640411), np.float64(0.48233441148883416)]\n", "Elapsed : 0.05704069137573242 \n", "\n", "Calling the function sequentially : \n", "\t [np.float64(0.5590277202277464), np.float64(0.8314047491898819), np.float64(0.42835223063884925), np.float64(0.3263644456888844), np.float64(0.710442560133207), np.float64(0.406870840521136), np.float64(0.5407012538640411), np.float64(0.48233441148883416)]\n", "Elapsed : 0.40640807151794434 \n", "\n", "True\n" ] } ], "source": [ "# Here, we vectorize `Objective.objective_function` using 8 threads, with the `b` parameter in the Objective class constructor set to `0.2`.\n", "t_parallel = mpy.Thread_Parallel(8, Objective, 0.2) \n", "\n", "#You can test the vectorization as follows : \n", "X = np.random.rand(8, 8) #randomly creates 6 vectors of dimension 8\n", "start = time.time()\n", "res = t_parallel(X) \n", "print(\"Vectorization using Thread_Parallel : \\n\\t\", res) \n", "print(\"Elapsed : \", time.time()-start, \"\\n\")\n", "\n", "obj=Objective(b=0.2)\n", "start = time.time()\n", "res2 = [obj.objective_function(x) for x in X]\n", "print(\"Calling the function sequentially : \\n\\t\", res2) \n", "print(\"Elapsed : \", time.time()-start, \"\\n\")\n", "\n", "#test if res and res2 are exactly the same \n", "print(np.array(res).all() == np.array(res2).all())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Minimize using one of minionpy algorithms" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Algo : ARRDE \n", "\t x : [5.018454243404394, -4.810866184531413, 9.992310013708282, 4.967835596117807, -4.803662393455747, 4.858113945288457, 4.438874376295008, 5.042549004109493] \n", "\t f(x) : -7.67703489988693 \n", "\t Elapsed: 9.916847229003906 seconds\n", "\n", "Test function value : -7.67703489988693\n" ] } ], "source": [ "dimension = 8 #set dimension of the problem\n", "maxevals = 1000 #number of function calls\n", "x0 = [[3.0]*dimension] # initial guess\n", "bounds = [(-10, 10)]*dimension\n", "algo = \"ARRDE\" \n", "\n", "now = time.time()\n", "min = mpy.Minimizer(func=t_parallel, x0=x0, bounds=bounds, algo=algo, relTol=0.0,\n", " maxevals=maxevals, callback=None, seed=None, options={\"population_size\": 0})\n", "result = min.optimize()\n", "elapsed= time.time()-now\n", "print(\"Algo : \",algo, \"\\n\\t x : \", result.x, \"\\n\\t f(x) : \", result.fun, \"\\n\\t Elapsed: \", elapsed, \" seconds\\n\")\n", "print(\"Test function value : \", Objective(b=0.2).objective_function(np.asarray(result.x)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe that the minimum found by the ARRDE algorithm corresponds to the correct function value. But what happens if we use ThreadPoolExecutor without considering the thread-safety of the objective_function method?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Algo : ARRDE \n", "\t x : [-9.997223468122773, -7.551520998748705, -7.93736170073008, -9.950709131816918, -9.91122046515175, 9.804161242381989, 0.8936974111335916, 7.752642571805346] \n", "\t f(x) : -7.188477780475587 \n", "\t Elapsed: 9.90553092956543 seconds\n", "\n", "Test function value : 0.9705624435760034\n" ] } ], "source": [ "obj = Objective(b=0.2)\n", "executor = concurrent.futures.ThreadPoolExecutor(max_workers=8)\n", "\n", "def vectorize_obj(X) : \n", " ret = list(executor.map(obj.objective_function, np.asarray(X)))\n", " return ret\n", "\n", "now = time.time()\n", "min = mpy.Minimizer(func=vectorize_obj, x0=x0, bounds=bounds, algo=algo, relTol=0.0, maxevals=maxevals, callback=None, seed=None, options=None)\n", "result = min.optimize()\n", "elapsed= time.time()-now\n", "print(\"Algo : \",algo, \"\\n\\t x : \", result.x, \"\\n\\t f(x) : \", result.fun, \"\\n\\t Elapsed: \", elapsed, \" seconds\\n\")\n", "print(\"Test function value :\", obj.objective_function(np.asarray(result.x)))\n", "executor.shutdown(wait=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the function value of the minimum is not the same as the actual function value. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using multiprocessing for non-thread-safe functions using `minionpy.Process_Parallel`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If multiprocessing is preferred over multithreading, whether to bypass the Global Interpreter Lock (GIL) or to ensure clean separation of data during objective function vectorization, `minionpy` provides the `Process_Parallel` feature. It follows the same usage rules as `Thread_Parallel`. When using `Process_Parallel`, a predefined number of reusable processes are created, each with its own instance of the class object. This approach works correctly, but it is best demonstrated from a separate Python script rather than directly from a notebook cell. On many platforms, multiprocessing workers must be able to import the objective class from a real module, so the example should be placed in a standalone file and executed under `if __name__ == \"__main__\":`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Note : the following snippet should be used in a separate script under if __name__ == \"__main__\", as Process_Parallel does not work in Jupyter notebooks.\n", "p_parallel = mpy.Process_Parallel(8, Objective, 0.2) \n", "\n", "#You can test the vectorization as follows : \n", "start = time.time()\n", "X = np.random.rand(8, 6) #randomly creates 6 vectors of dimension 8\n", "res = p_parallel(X) \n", "print(\"Vectorization using Process_Parallel : \\n\\t\", res) \n", "print(\"Elapsed : \", time.time()-start, \"\\n\")\n", "\n", "obj=Objective(b=0.2)\n", "start = time.time()\n", "res2 = [obj.objective_function(x) for x in X]\n", "print(\"Calling the function sequentially : \\n\\t\", res2) \n", "print(\"Elapsed : \", time.time()-start, \"\\n\")\n", "\n", "#test if res and res2 are exactly the same \n", "print(np.array(res).all() == np.array(res2).all())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Note : the following snippet should be used in a separate script under if __name__ == \"__main__\", as Process_Parallel does not work in Jupyter notebooks.\n", "dimension = 8 #set dimension of the problem\n", "maxevals = 1000 #number of function calls\n", "x0 = [[3.0]*dimension] # initial guess\n", "bounds = [(-10, 10)]*dimension\n", "algo = \"ARRDE\" \n", "\n", "now = time.time()\n", "min = mpy.Minimizer(func=p_parallel, x0=x0, bounds=bounds, algo=algo, relTol=0.0,\n", " maxevals=maxevals, callback=None, seed=None, options={\"population_size\": 0})\n", "result = min.optimize()\n", "elapsed= time.time()-now\n", "print(\"Algo : \",algo, \"\\n\\t x : \", result.x, \"\\n\\t f(x) : \", result.fun, \"\\n\\t Elapsed: \", elapsed, \" seconds\\n\")\n", "print(\"Test function value : \", Objective(b=0.2).objective_function(np.asarray(result.x)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Algorithm Comparisons Using CEC Benchmark Problems\n", "\n", "We can compare the performance of different optimization algorithms by evaluating them on benchmark problems from the Congress on Evolutionary Computation (CEC) competition. The Minion library provides implementations of benchmark problems from the following CEC years: 2011, 2014, 2017, 2019, 2020, and 2022.\n", "\n", "- **CEC2014 and CEC2017**: These benchmarks contain 30 problems, implemented for dimensions 10, 20, 30, 50, and 100.\n", "- **CEC2019**: This set includes 10 problems with varying dimensions.\n", "- **CEC2020**: It contains 10 problems with dimensions 5, 10, 15, and 20.\n", "- **CEC2022**: This set consists of 12 problems with dimensions 10 and 20.\n", "\n", "CEC problems typically include a variety of function types:\n", "- **Basic functions** (e.g., Rosenbrock, Rastrigin),\n", "- **Hybrid functions** (new functions constructed by combining basic functions, where each component is evaluated using a different basic function),\n", "- **Composite functions** (linear combinations of basic functions, where the coefficients are also functions of the input vector).\n", "\n", "These functions are often shifted and rotated to introduce additional complexity.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Running optimization for func_1 (Dimension: 20)\n", "============================================================\n", " DE f(x): 1035.1009 \n", " ABC f(x): 49967.702 \n", " LSHADE f(x): 300.74801 \n", " LSHADE_cnEpSin f(x): 300.03523 \n", " JADE f(x): 457.20224 \n", " jSO f(x): 300.36146 \n", " j2020 f(x): 27562.584 \n", " LSRTDE f(x): 300.58607 \n", " NLSHADE_RSP f(x): 6019.1092 \n", " IMODE f(x): 300 \n", " AGSK f(x): 14275.397 \n", " ARRDE f(x): 300.00032 \n", " GWO_DE f(x): 4560.7819 \n", " NelderMead f(x): 358.27996 \n", " DA f(x): 300 \n", " L_BFGS_B f(x): 300 \n", " PSO f(x): 3761.7453 \n", " DMSPSO f(x): 756.64802 \n", " SPSO2011 f(x): 10320.75 \n", " CMAES f(x): 300 \n", " BIPOP_aCMAES f(x): 300 \n", " RCMAES f(x): 4654.8386 \n", " Scipy L-BFGS-B f(x): 300 \n", " Scipy Nelder-Mead f(x): 9928.5338 \n", " Scipy DA f(x): 300 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_2 (Dimension: 20)\n", "============================================================\n", " DE f(x): 475.10938 \n", " ABC f(x): 487.65906 \n", " LSHADE f(x): 449.08457 \n", " LSHADE_cnEpSin f(x): 449.08448 \n", " JADE f(x): 449.08465 \n", " jSO f(x): 449.08451 \n", " j2020 f(x): 449.0931 \n", " LSRTDE f(x): 449.08673 \n", " NLSHADE_RSP f(x): 453.61644 \n", " IMODE f(x): 412.42697 \n", " AGSK f(x): 449.09543 \n", " ARRDE f(x): 444.89547 \n", " GWO_DE f(x): 449.5273 \n", " NelderMead f(x): 449.38295 \n", " DA f(x): 449.08448 \n", " L_BFGS_B f(x): 449.08448 \n", " PSO f(x): 474.39471 \n", " DMSPSO f(x): 449.08448 \n", " SPSO2011 f(x): 449.61382 \n", " CMAES f(x): 449.08448 \n", " BIPOP_aCMAES f(x): 449.70785 \n", " RCMAES f(x): 427.40889 \n", " Scipy L-BFGS-B f(x): 449.08448 \n", " Scipy Nelder-Mead f(x): 560.25712 \n", " Scipy DA f(x): 400 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_3 (Dimension: 20)\n", "============================================================\n", " DE f(x): 605.05102 \n", " ABC f(x): 610.74446 \n", " LSHADE f(x): 600.13947 \n", " LSHADE_cnEpSin f(x): 600.1087 \n", " JADE f(x): 600.00002 \n", " jSO f(x): 600.0122 \n", " j2020 f(x): 600.00238 \n", " LSRTDE f(x): 600.09935 \n", " NLSHADE_RSP f(x): 602.5856 \n", " IMODE f(x): 625.4722 \n", " AGSK f(x): 614.54725 \n", " ARRDE f(x): 600.00587 \n", " GWO_DE f(x): 600.71827 \n", " NelderMead f(x): 668.52549 \n", " DA f(x): 605.55265 \n", " L_BFGS_B f(x): 668.5255 \n", " PSO f(x): 602.59005 \n", " DMSPSO f(x): 600.37642 \n", " SPSO2011 f(x): 601.78052 \n", " CMAES f(x): 600.3345 \n", " BIPOP_aCMAES f(x): 600.53816 \n", " RCMAES f(x): 600.00004 \n", " Scipy L-BFGS-B f(x): 668.52551 \n", " Scipy Nelder-Mead f(x): 669.03505 \n", " Scipy DA f(x): 603.72526 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_4 (Dimension: 20)\n", "============================================================\n", " DE f(x): 921.55629 \n", " ABC f(x): 910.93957 \n", " LSHADE f(x): 854.27876 \n", " LSHADE_cnEpSin f(x): 833.08867 \n", " JADE f(x): 819.79005 \n", " jSO f(x): 854.87208 \n", " j2020 f(x): 919.296 \n", " LSRTDE f(x): 908.13509 \n", " NLSHADE_RSP f(x): 882.59239 \n", " IMODE f(x): 924.68848 \n", " AGSK f(x): 898.23827 \n", " ARRDE f(x): 807.95989 \n", " GWO_DE f(x): 892.09169 \n", " NelderMead f(x): 890.54093 \n", " DA f(x): 932.32807 \n", " L_BFGS_B f(x): 890.54093 \n", " PSO f(x): 835.81846 \n", " DMSPSO f(x): 835.81849 \n", " SPSO2011 f(x): 868.11726 \n", " CMAES f(x): 811.9395 \n", " BIPOP_aCMAES f(x): 838.80334 \n", " RCMAES f(x): 807.95968 \n", " Scipy L-BFGS-B f(x): 890.54093 \n", " Scipy Nelder-Mead f(x): 892.11966 \n", " Scipy DA f(x): 899.49524 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_5 (Dimension: 20)\n", "============================================================\n", " DE f(x): 1127.6864 \n", " ABC f(x): 3441.794 \n", " LSHADE f(x): 900.72922 \n", " LSHADE_cnEpSin f(x): 900.00023 \n", " JADE f(x): 900 \n", " jSO f(x): 900.08967 \n", " j2020 f(x): 900.25644 \n", " LSRTDE f(x): 900.0038 \n", " NLSHADE_RSP f(x): 1035.6895 \n", " IMODE f(x): 1975.8172 \n", " AGSK f(x): 1160.9526 \n", " ARRDE f(x): 900 \n", " GWO_DE f(x): 958.7145 \n", " NelderMead f(x): 2456.1107 \n", " DA f(x): 3133.3091 \n", " L_BFGS_B f(x): 2458.8607 \n", " PSO f(x): 1570.7013 \n", " DMSPSO f(x): 911.43741 \n", " SPSO2011 f(x): 900.05354 \n", " CMAES f(x): 900 \n", " BIPOP_aCMAES f(x): 900 \n", " RCMAES f(x): 900 \n", " Scipy L-BFGS-B f(x): 2458.8607 \n", " Scipy Nelder-Mead f(x): 2457.195 \n", " Scipy DA f(x): 2875.4063 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_6 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2718.2183 \n", " ABC f(x): 28669.942 \n", " LSHADE f(x): 1910.8788 \n", " LSHADE_cnEpSin f(x): 1855.173 \n", " JADE f(x): 1925.221 \n", " jSO f(x): 1884.693 \n", " j2020 f(x): 183901.13 \n", " LSRTDE f(x): 1844.8332 \n", " NLSHADE_RSP f(x): 1909.2953 \n", " IMODE f(x): 34398225 \n", " AGSK f(x): 102295.05 \n", " ARRDE f(x): 1860.0103 \n", " GWO_DE f(x): 66915.161 \n", " NelderMead f(x): 1899.6356 \n", " DA f(x): 1850.2409 \n", " L_BFGS_B f(x): 1875.3024 \n", " PSO f(x): 5513.8029 \n", " DMSPSO f(x): 1981.4143 \n", " SPSO2011 f(x): 2950.0372 \n", " CMAES f(x): 1952.0223 \n", " BIPOP_aCMAES f(x): 1830.306 \n", " RCMAES f(x): 1850.3832 \n", " Scipy L-BFGS-B f(x): 1871.7843 \n", " Scipy Nelder-Mead f(x): 2054.1894 \n", " Scipy DA f(x): 1812.5904 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_7 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2033.3604 \n", " ABC f(x): 2093.5065 \n", " LSHADE f(x): 2063.4361 \n", " LSHADE_cnEpSin f(x): 2052.696 \n", " JADE f(x): 2034.8118 \n", " jSO f(x): 2052.9419 \n", " j2020 f(x): 2057.4182 \n", " LSRTDE f(x): 2025.5151 \n", " NLSHADE_RSP f(x): 2033.4797 \n", " IMODE f(x): 2085.0425 \n", " AGSK f(x): 2103.7497 \n", " ARRDE f(x): 2025.044 \n", " GWO_DE f(x): 2043.1313 \n", " NelderMead f(x): 2529.0241 \n", " DA f(x): 2068.3336 \n", " L_BFGS_B f(x): 2528.1893 \n", " PSO f(x): 2101.0439 \n", " DMSPSO f(x): 2023.523 \n", " SPSO2011 f(x): 2084.381 \n", " CMAES f(x): 2049.4525 \n", " BIPOP_aCMAES f(x): 2094.9708 \n", " RCMAES f(x): 2021.3803 \n", " Scipy L-BFGS-B f(x): 2597.5477 \n", " Scipy Nelder-Mead f(x): 2617.3528 \n", " Scipy DA f(x): 2055.0225 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_8 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2226.1043 \n", " ABC f(x): 2226.2005 \n", " LSHADE f(x): 2229.3323 \n", " LSHADE_cnEpSin f(x): 2232.1618 \n", " JADE f(x): 2224.2439 \n", " jSO f(x): 2232.4331 \n", " j2020 f(x): 2231.5721 \n", " LSRTDE f(x): 2228.759 \n", " NLSHADE_RSP f(x): 2225.7131 \n", " IMODE f(x): 2248.8373 \n", " AGSK f(x): 2233.2377 \n", " ARRDE f(x): 2228.115 \n", " GWO_DE f(x): 2233.6226 \n", " NelderMead f(x): 2636.88 \n", " DA f(x): 2222.3222 \n", " L_BFGS_B f(x): 2961.2446 \n", " PSO f(x): 2339.927 \n", " DMSPSO f(x): 2340.5193 \n", " SPSO2011 f(x): 2231.8724 \n", " CMAES f(x): 2224.9362 \n", " BIPOP_aCMAES f(x): 2221.0861 \n", " RCMAES f(x): 2227.8191 \n", " Scipy L-BFGS-B f(x): 2902.8126 \n", " Scipy Nelder-Mead f(x): 2890.3671 \n", " Scipy DA f(x): 2343.1096 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_9 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2506.9626 \n", " ABC f(x): 2486.7919 \n", " LSHADE f(x): 2480.7814 \n", " LSHADE_cnEpSin f(x): 2480.7813 \n", " JADE f(x): 2480.7813 \n", " jSO f(x): 2480.7813 \n", " j2020 f(x): 2480.7819 \n", " LSRTDE f(x): 2480.8057 \n", " NLSHADE_RSP f(x): 2482.3874 \n", " IMODE f(x): 2552.7522 \n", " AGSK f(x): 2480.7847 \n", " ARRDE f(x): 2480.7813 \n", " GWO_DE f(x): 2481.0204 \n", " NelderMead f(x): 2482.171 \n", " DA f(x): 2480.7813 \n", " L_BFGS_B f(x): 2480.7813 \n", " PSO f(x): 2480.7813 \n", " DMSPSO f(x): 2480.7813 \n", " SPSO2011 f(x): 2489.369 \n", " CMAES f(x): 2480.7813 \n", " BIPOP_aCMAES f(x): 2482.3772 \n", " RCMAES f(x): 2484.5006 \n", " Scipy L-BFGS-B f(x): 2480.7813 \n", " Scipy Nelder-Mead f(x): 2567.0897 \n", " Scipy DA f(x): 2480.7813 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_10 (Dimension: 20)\n", "============================================================\n", " DE f(x): 3653.3005 \n", " ABC f(x): 2501.1775 \n", " LSHADE f(x): 2500.4803 \n", " LSHADE_cnEpSin f(x): 2500.6496 \n", " JADE f(x): 2500.5933 \n", " jSO f(x): 2500.5421 \n", " j2020 f(x): 2512.5501 \n", " LSRTDE f(x): 2500.5561 \n", " NLSHADE_RSP f(x): 2500.7545 \n", " IMODE f(x): 2501.2848 \n", " AGSK f(x): 2500.7967 \n", " ARRDE f(x): 2500.5408 \n", " GWO_DE f(x): 2500.5993 \n", " NelderMead f(x): 6169.0617 \n", " DA f(x): 2726.4177 \n", " L_BFGS_B f(x): 6109.5459 \n", " PSO f(x): 3512.719 \n", " DMSPSO f(x): 3088.8943 \n", " SPSO2011 f(x): 2500.8714 \n", " CMAES f(x): 3380.158 \n", " BIPOP_aCMAES f(x): 2625.1251 \n", " RCMAES f(x): 2500.5443 \n", " Scipy L-BFGS-B f(x): 6287.0682 \n", " Scipy Nelder-Mead f(x): 5716.4003 \n", " Scipy DA f(x): 2400.2811 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_11 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2933.2513 \n", " ABC f(x): 3287.1576 \n", " LSHADE f(x): 2900.0364 \n", " LSHADE_cnEpSin f(x): 2900.0333 \n", " JADE f(x): 2900 \n", " jSO f(x): 2900.0085 \n", " j2020 f(x): 2900.0082 \n", " LSRTDE f(x): 2900.4144 \n", " NLSHADE_RSP f(x): 2902.5872 \n", " IMODE f(x): 3287.6097 \n", " AGSK f(x): 2904.9657 \n", " ARRDE f(x): 3000 \n", " GWO_DE f(x): 2931.5272 \n", " NelderMead f(x): 3000 \n", " DA f(x): 3000 \n", " L_BFGS_B f(x): 2900 \n", " PSO f(x): 2900 \n", " DMSPSO f(x): 2900 \n", " SPSO2011 f(x): 2900.0047 \n", " CMAES f(x): 2900 \n", " BIPOP_aCMAES f(x): 2900 \n", " RCMAES f(x): 2900 \n", " Scipy L-BFGS-B f(x): 2900 \n", " Scipy Nelder-Mead f(x): 3510.1295 \n", " Scipy DA f(x): 2900 \n", "------------------------------------------------------------\n", "\n", "Running optimization for func_12 (Dimension: 20)\n", "============================================================\n", " DE f(x): 2987.3575 \n", " ABC f(x): 2965.7603 \n", " LSHADE f(x): 2935.9888 \n", " LSHADE_cnEpSin f(x): 2933.2037 \n", " JADE f(x): 2938.0283 \n", " jSO f(x): 2932.2629 \n", " j2020 f(x): 2950.5141 \n", " LSRTDE f(x): 2940.1399 \n", " NLSHADE_RSP f(x): 2977.4347 \n", " IMODE f(x): 3069.1483 \n", " AGSK f(x): 2941.6012 \n", " ARRDE f(x): 2942.1424 \n", " GWO_DE f(x): 2946.1216 \n", " NelderMead f(x): 5490.2128 \n", " DA f(x): 2976.6605 \n", " L_BFGS_B f(x): 5490.2128 \n", " PSO f(x): 2949.3587 \n", " DMSPSO f(x): 2979.1232 \n", " SPSO2011 f(x): 2975.7828 \n", " CMAES f(x): 2950.6458 \n", " BIPOP_aCMAES f(x): 3000.716 \n", " RCMAES f(x): 2963.7624 \n", " Scipy L-BFGS-B f(x): 3434.9771 \n", " Scipy Nelder-Mead f(x): 5188.5097 \n", " Scipy DA f(x): 2989.4664 \n", "------------------------------------------------------------\n" ] } ], "source": [ "import threading\n", "import concurrent.futures\n", "\n", "# This script minimizes CEC benchmark problems, repeated for NRuns times.\n", "\n", "# Global results variable\n", "results = []\n", "results_lock = threading.Lock()\n", "\n", "def test_optimization(func, bounds, dimension, func_name, Nmaxeval, seed):\n", " \"\"\"Runs optimization algorithms on a given function and stores the results.\"\"\"\n", " global results\n", " result = {\n", " \"Dimensions\": dimension,\n", " \"Function\": func_name\n", " }\n", " \n", " x0 = [[0.0 for _ in range(len(bounds))]]\n", "\n", " print(f\"\\nRunning optimization for {func_name} (Dimension: {dimension})\")\n", " print(\"=\" * 60)\n", "\n", " for algo in algos:\n", " res = mpy.Minimizer(\n", " func, bounds, x0=x0, relTol=0.0, algo=algo, \n", " maxevals=Nmaxeval, callback=None, seed=None, \n", " options={\n", " \"population_size\" : 0, #2*dimension, \n", " \"func_noise_ratio\" : 0.0, \n", " \"N_points_derivative\": 1, \n", " \"bound_strategy\" : \"reflect-random\"\n", " }\n", " ).optimize()\n", " result[algo] = res.fun\n", " print(f\" {algo:<15} f(x): {res.fun:<20.8g}\")\n", "\n", " def func_scipy(par):\n", " return func([par])[0]\n", " \n", " # SciPy Optimizers\n", " scipy_algorithms = [\n", " (\"Scipy L-BFGS-B\", minimize, {\"x0\": x0[0], \"method\": \"L-BFGS-B\", \"options\": {\"maxfun\": Nmaxeval}, \"bounds\": bounds}),\n", " (\"Scipy Nelder-Mead\", minimize, {\"x0\": x0[0], \"method\": \"Nelder-Mead\", \"bounds\": bounds, \"options\": {\"maxfev\": Nmaxeval, \"adaptive\": True}}),\n", " (\"Scipy DA\", dual_annealing, {\"bounds\": bounds, \"maxfun\": Nmaxeval, \"no_local_search\": False, \"x0\": x0[0]}),\n", " ]\n", "\n", " for name, func_opt, kwargs in scipy_algorithms:\n", " res = func_opt(func_scipy, **kwargs)\n", " result[name] = res.fun\n", " print(f\" {name:<15} f(x): {res.fun:<20.8g}\")\n", "\n", " with results_lock:\n", " results.append(result)\n", "\n", " print(\"-\" * 60)\n", "\n", "def run_test_optimization(j, dim, year=2017, seed=None):\n", " \"\"\"Runs the optimization for a specific function and CEC benchmark year.\"\"\"\n", " cec_func_classes = {\n", " 2011 : mpy.CEC2011Functions,\n", " 2014: mpy.CEC2014Functions,\n", " 2017: mpy.CEC2017Functions,\n", " 2019: mpy.CEC2019Functions,\n", " 2020: mpy.CEC2020Functions,\n", " 2022: mpy.CEC2022Functions\n", " }\n", " \n", " if year not in cec_func_classes:\n", " raise Exception(\"Unknown CEC year.\")\n", " \n", " cec_func = cec_func_classes[year](function_number=j, dimension=dim)\n", " bounds = [(-100, 100)] * dimension\n", " if year == 2011 : \n", " bounds = cec_func.get_bounds()\n", " test_optimization(cec_func,bounds, dim, f\"func_{j}\", Nmaxeval, seed)\n", "\n", "# List of algorithms to be tested\n", "algos = [\n", " \"DE\", \"ABC\", \"LSHADE\", \"LSHADE_cnEpSin\", \"JADE\", \"jSO\", \"j2020\", \"LSRTDE\", \"NLSHADE_RSP\", \"IMODE\", \"AGSK\",\n", " \"ARRDE\", \"GWO_DE\", \"NelderMead\", \"DA\", \"L_BFGS_B\", \"PSO\", \"DMSPSO\", \"SPSO2011\", \"CMAES\", \"BIPOP_aCMAES\", \"RCMAES\"\n", "]\n", "\n", "Nmaxeval = 20000 # Maximum number of function evaluations\n", "dimension = 20\n", "NRuns = 1 # Number of repetitions\n", "year = 2022 # CEC benchmark year\n", "\n", "# Function numbers for each CEC year\n", "func_numbers_dict = {\n", " 2022: list(range(1, 13)),\n", " 2020: list(range(1, 11)),\n", " 2019: list(range(1, 11)),\n", " 2017: list(range(1, 31)),\n", " 2014: list(range(1, 31)),\n", " 2011 : list(range(1, 23))\n", "}\n", "func_numbers = func_numbers_dict[year]\n", "\n", "# Run optimizations using multi-threading\n", "with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:\n", " futures = [\n", " executor.submit(run_test_optimization, j, dimension, year, k)\n", " for k in range(NRuns)\n", " for j in func_numbers\n", " ]\n", " concurrent.futures.wait(futures)\n", " \n", " for f in futures:\n", " f.result()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example of using minion/py in curve fitting problems\n", "\n", "\n", "Here, an example of using minion to minimize an objective function related to a curve fitting problem is demonstrated. \n", "The idea is first to define the data generation model, generate the data, fit the model, and report the result. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polynomial Fitting Problems\n", "In this problem, we try fit a polynomial from a set of data. Specifically, a set of points ($\\{(x_i, y_i) \\mid i = 1, 2, \\dots, N\\}$) with $x \\in [0, 1]$ is generated according to:\n", "$$\n", " f(x, a) = \\sum_{j=0}^{D-1} a_j x^j\n", "$$\n", "Given the coefficients $a_j$ that generate the data, the goal is to reproduce the data points by minimizing the objective function:\n", "$$\n", " L = \\frac{1}{N}\\sum_{i=1}^N \\left(y_i - f(x_i, a)\\right)^2 \n", "$$\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Optimization Results:\n", "==================================================\n", "DE : f(x) = 1.4440892e-08 \n", "ABC : f(x) = 0.0056994221 \n", "LSHADE : f(x) = 1.747296e-12 \n", "LSHADE_cnEpSin : f(x) = 1.2546628e-13 \n", "JADE : f(x) = 3.9365631e-08 \n", "jSO : f(x) = 1.7792886e-11 \n", "j2020 : f(x) = 8.4535903e-05 \n", "LSRTDE : f(x) = 2.545778e-18 \n", "NLSHADE_RSP : f(x) = 1.0265447e-05 \n", "ARRDE : f(x) = 7.7391481e-10 \n", "GWO_DE : f(x) = 7.7970011e-05 \n", "NelderMead : f(x) = 4.0539659e-15 \n", "DA : f(x) = 7.3320295e-07 \n", "L_BFGS_B : f(x) = 7.3320324e-07 \n", "PSO : f(x) = 7.9373746e-06 \n", "DMSPSO : f(x) = 0.00024702002 \n", "SPSO2011 : f(x) = 0.00013752664 \n", "CMAES : f(x) = 9.8216578e-13 \n", "BIPOP_aCMAES : f(x) = 5.4117211e-22 \n", "RCMAES : f(x) = 1.1677759e-13 \n", "Scipy Dual Annealing (DA) : f(x) = 7.3247761e-07 \n", "Scipy L-BFGS-B : f(x) = 7.3247761e-07 \n", "Scipy Nelder-Mead : f(x) = 2.1676521e-11 \n", "==================================================\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Step 1: Generate Data Points from a Polynomial\n", "dimension = 10 # Number of free parameters (polynomial degree is dimension - 1)\n", "np.random.seed(8) # For reproducibility\n", "\n", "# True polynomial coefficients (random values)\n", "true_coefficients = [np.random.uniform(-1.0, 1.0) * (1.0 ** i) for i in range(dimension)]\n", "\n", "# Generate data points\n", "x_data = np.linspace(0.0, 1, dimension + 10)\n", "y_data = np.polyval(true_coefficients, x_data)\n", "\n", "# Step 2: Define the Polynomial Model\n", "def polynomial_model(x, coefficients):\n", " \"\"\"Evaluate a polynomial at x given the coefficients.\"\"\"\n", " return np.polyval(coefficients, x)\n", "\n", "# Step 3: Define the Objective Function\n", "def objective_function(coefficients):\n", " \"\"\"Compute the mean squared error between the polynomial model and data points.\"\"\"\n", " y_pred = polynomial_model(x_data, coefficients)\n", " return np.mean((y_data - y_pred) ** 2)\n", "\n", "def objective_function_vect(X):\n", " \"\"\"Vectorized version of the objective function for batch optimization.\"\"\"\n", " return [objective_function(x) for x in X]\n", "\n", "# Optimization Settings\n", "bounds = [(-10, 10)] * dimension\n", "Nmaxeval = 20000\n", "algos = [\n", " \"DE\", \"ABC\", \"LSHADE\", \"LSHADE_cnEpSin\", \"JADE\", \"jSO\", \"j2020\", \"LSRTDE\", \"NLSHADE_RSP\",\n", " \"ARRDE\", \"GWO_DE\", \"NelderMead\", \"DA\", \"L_BFGS_B\", \"PSO\", \"DMSPSO\", \"SPSO2011\", \"CMAES\", \"BIPOP_aCMAES\", \"RCMAES\"\n", "]\n", "\n", "# Step 4: Minimize the Objective Function and Plot Results\n", "plt.figure(figsize=(6, 4))\n", "plt.scatter(x_data, y_data, label=\"Data Points\", color=\"black\", marker=\"o\", zorder=3)\n", "\n", "x0 = [[0.0 for _ in range(dimension)]]\n", "\n", "print(\"\\nOptimization Results:\")\n", "print(\"=\" * 50)\n", "\n", "# Run Optimization with Custom Algorithms\n", "for algo in algos:\n", " res = mpy.Minimizer(\n", " objective_function_vect, bounds, x0=x0, relTol=0.0,\n", " algo=algo, maxevals=Nmaxeval, callback=None, seed=0,\n", " options={\n", " \"population_size\": 0, \n", " \"func_noise_ratio\" : 0.0, \n", " \"N_points_derivative\": 1}\n", " ).optimize()\n", "\n", " print(f\"{algo:<30}: f(x) = {res.fun:<20.8g}\")\n", " plt.plot(x_data, polynomial_model(x_data, res.x), label=algo, linewidth=1.2, alpha=0.7)\n", "\n", "# Run SciPy Optimizers\n", "scipy_algorithms = [\n", " (\"Scipy Dual Annealing (DA)\", dual_annealing, {\"bounds\": bounds, \"maxfun\": Nmaxeval, \"no_local_search\": False, \"x0\": x0[0]}),\n", " (\"Scipy L-BFGS-B\", minimize, {\"x0\": x0[0], \"bounds\": bounds, \"method\": \"L-BFGS-B\", \"options\": {\"maxfun\": Nmaxeval}}),\n", " (\"Scipy Nelder-Mead\", minimize, {\"x0\": x0[0], \"bounds\": bounds, \"method\": \"Nelder-Mead\", \"options\": {\"maxfev\": Nmaxeval, \"adaptive\": True}}),\n", "]\n", "\n", "for name, func_opt, kwargs in scipy_algorithms:\n", " res = func_opt(objective_function, **kwargs)\n", " print(f\"{name:<30}: f(x) = {res.fun:<20.8g}\")\n", " plt.plot(x_data, polynomial_model(x_data, res.x), label=name, linewidth=1.2, alpha=0.7)\n", "\n", "print(\"=\" * 50)\n", "\n", "# Plot Formatting\n", "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1), fontsize=9)\n", "plt.title(\"Polynomial Fit\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.grid(True, linestyle=\"--\", alpha=0.6)\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Mixture Model Fitting Problems\n", "\n", "Thsi time, the model is given by the sum of Gaussian functions:\n", "$$\n", " f(x, a, b, c) = \\sum_{j=1}^{D/3} \\frac{a_j}{\\sum_{k=1}^{D/3} a_k} \\frac{1}{b_j \\sqrt{2\\pi}} \\exp\\left[-\\frac{1}{2} \\frac{(x-c_j)^2}{b_j^2}\\right]\n", "$$\n", "Here, $f(x, a, b, c)$ is normalized to represent a probability distribution. The data points are generated using predefined values of $a_j$, $b_j$, and $c_j$ within the interval $x \\in [-20, 20]$. The objective function is the same as in the case of polynomial fitting. \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Optimization Results:\n", "============================================================\n", "DE : f(x) = 5.2016821e-08 \n", "ABC : f(x) = 1.3409008e-06 \n", "LSHADE : f(x) = 5.5908417e-07 \n", "LSHADE_cnEpSin : f(x) = 1.2469598e-06 \n", "JADE : f(x) = 7.0607139e-08 \n", "jSO : f(x) = 8.4092472e-07 \n", "j2020 : f(x) = 1.5717859e-07 \n", "LSRTDE : f(x) = 4.7937334e-06 \n", "NLSHADE_RSP : f(x) = 7.8347728e-07 \n", "ARRDE : f(x) = 5.8702427e-09 \n", "GWO_DE : f(x) = 9.5340273e-09 \n", "NelderMead : f(x) = 7.3549528e-09 \n", "DA : f(x) = 7.8090419e-06 \n", "L_BFGS_B : f(x) = 2.5552938e-05 \n", "PSO : f(x) = 8.7937321e-09 \n", "DMSPSO : f(x) = 8.8752675e-10 \n", "SPSO2011 : f(x) = 2.4620218e-06 \n", "CMAES : f(x) = 3.093182e-08 \n", "BIPOP_aCMAES : f(x) = 1.5690981e-10 \n", "Scipy L-BFGS-B : f(x) = 2.4099371e-05 \n", "Scipy Dual Annealing : f(x) = 6.1466904e-06 \n", "Scipy Nelder-Mead : f(x) = 4.1184114e-09 \n", "============================================================\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize, dual_annealing\n", "import concurrent.futures\n", "\n", "# Step 1: Generate Data Points from a Gaussian Mixture Model (GMM)\n", "np.random.seed(5) # For reproducibility\n", "\n", "num_gauss = 5 # Number of Gaussians\n", "true_centers = 10 * (-1 + 2 * np.random.random(num_gauss)) # Random center positions\n", "true_widths = np.random.rand(num_gauss) + 1.0 # Widths (variances)\n", "true_coeffs = 2.0 * np.random.rand(num_gauss) # Coefficients\n", "\n", "dimension = num_gauss * 3 # Total number of free parameters\n", "\n", "# Define a Gaussian function\n", "def gauss(x, center, width):\n", " \"\"\"Compute a Gaussian value at x given center and width.\"\"\"\n", " return (1.0 / (2.0 * np.pi * width ** 2)) * 0.5 * np.exp(-((x - center) ** 2) / (2 * width ** 2))\n", "\n", "# Define the Gaussian Mixture Model (GMM)\n", "def gmm(x, centers, widths, coeffs):\n", " \"\"\"Evaluate the Gaussian Mixture Model (GMM) at x.\"\"\"\n", " result = np.zeros_like(x)\n", " coeffs = np.array(coeffs)\n", " norm_coeff = coeffs / np.sum(coeffs) # Normalize coefficients\n", " for i in range(len(centers)):\n", " result += norm_coeff[i] * gauss(x, centers[i], widths[i])\n", " return result\n", "\n", "# Generate synthetic data\n", "x_data = np.linspace(-20, 20, dimension + 10)\n", "y_data = gmm(x_data, true_centers, true_widths, true_coeffs)\n", "\n", "# Step 2: Define the Model for Fitting\n", "def gmm_model(x, params):\n", " \"\"\"Compute GMM model values for given parameters.\"\"\"\n", " num_gauss = len(params) // 3\n", " centers = params[:num_gauss]\n", " widths = params[num_gauss:2*num_gauss]\n", " coeffs = params[2*num_gauss:]\n", " return gmm(x, centers, widths, coeffs)\n", "\n", "# Step 3: Define the Objective Function\n", "def objective_function(params):\n", " \"\"\"Compute the mean squared error between model and data.\"\"\"\n", " y_pred = gmm_model(x_data, params)\n", " return np.mean((y_data - y_pred) ** 2)\n", "\n", "# Parallelized objective function\n", "executor = concurrent.futures.ThreadPoolExecutor(max_workers=8)\n", "\n", "def objective_function_vect(params):\n", " \"\"\"Vectorized version of objective function using multithreading.\"\"\"\n", " return list(executor.map(objective_function, params))\n", "\n", "# Optimization Settings\n", "bounds = [(-10, 10)] * dimension\n", "Nmaxeval = 10000\n", "algos = [\n", " \"DE\", \"ABC\", \"LSHADE\", \"LSHADE_cnEpSin\", \"JADE\", \"jSO\", \"j2020\", \"LSRTDE\", \"NLSHADE_RSP\",\n", " \"ARRDE\", \"GWO_DE\", \"NelderMead\", \"DA\", \"L_BFGS_B\", \"PSO\", \"DMSPSO\", \"SPSO2011\", \"CMAES\", \"BIPOP_aCMAES\"\n", "]\n", "\n", "x0 = [[1.0 for _ in range(dimension)]]\n", "\n", "# Step 4: Minimize the Objective Function and Plot Results\n", "plt.figure(figsize=(6, 4))\n", "plt.scatter(x_data, y_data, label=\"Data\", color=\"black\", marker=\"o\", zorder=3)\n", "\n", "print(\"\\nOptimization Results:\")\n", "print(\"=\" * 60)\n", "\n", "# Run Optimization with Custom Algorithms\n", "for algo in algos:\n", " res = mpy.Minimizer(\n", " objective_function_vect, bounds, x0=x0, relTol=0.0,\n", " algo=algo, maxevals=Nmaxeval, callback=None, seed=None,\n", " options={\"population_size\": 0}\n", " ).optimize()\n", "\n", " print(f\"{algo:<30}: f(x) = {res.fun:<20.8g}\")\n", " plt.plot(x_data, gmm_model(x_data, res.x), label=algo, linewidth=1.2, alpha=0.7)\n", "\n", "# Run SciPy Optimizers\n", "scipy_algorithms = [\n", " (\"Scipy L-BFGS-B\", minimize, {\"x0\": x0[0], \"bounds\": bounds, \"method\": \"L-BFGS-B\", \"options\": {\"maxfun\": Nmaxeval}}),\n", " (\"Scipy Dual Annealing\", dual_annealing, {\"x0\": x0[0],\"bounds\": bounds, \"maxfun\": Nmaxeval, \"no_local_search\": False}),\n", " (\"Scipy Nelder-Mead\", minimize, {\"x0\": x0[0], \"bounds\": bounds,\"method\": \"Nelder-Mead\", \"options\": {\"maxfev\": Nmaxeval, \"adaptive\": True}}),\n", "]\n", "\n", "for name, func_opt, kwargs in scipy_algorithms:\n", " res = func_opt(objective_function, **kwargs)\n", " print(f\"{name:<30}: f(x) = {res.fun:<20.8g}\")\n", " plt.plot(x_data, gmm_model(x_data, res.x), label=name, linewidth=1.2, alpha=0.7)\n", "\n", "print(\"=\" * 60)\n", "\n", "# Plot Formatting\n", "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1), fontsize=9)\n", "plt.title(\"Gaussian Mixture Model Fit\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.grid(True, linestyle=\"--\", alpha=0.6)\n", "plt.show()\n", "\n", "executor.shutdown()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More complex fitting problem: CT18 PDFs Fitting\n", "\n", "Here, we provide a slightly more challenging curve fitting problem. The task is to reproduce the CT18 parton distribution functions (PDFs) \\cite{Hou:2019efy}. The parameterization for valence up-quark ($u_v$), valence down-quark ($d_v$), gluon, anti-$u$ ($\\bar{u}$), anti-$d$ ($\\bar{d}$), and strange quark ($s$) PDFs at the initial scale is given by:\n", "$$\n", " f_i(x) = a_0 x^{a_1-1} (1-x)^{a_2} P_i(y, a_3, a_4, \\dots), \\quad i \\in \\{u_v, d_v, g, \\bar{u}, \\bar{d}, s\\}\n", "$$\n", "Here, $P_i(y)$ is a Bernstein polynomial of degree 4, 3, or 5, depending on the specific PDF. The variable $y$ is defined as $y = \\sqrt{x}$ for $u_v$, $d_v$, and $g$, and as $y = (1 - (1 - \\sqrt{x}))^{a_3}$ for the sea quarks. The objective function is:\n", "$$\n", " L = \\sum_i \\frac{1}{N} \\sum_{j=1}^N \\left(y_j - f_i(x_j)\\right)^2\n", "$$\n", "where $i \\in \\{u_v, d_v, g, \\bar{u}, \\bar{d}, s\\}$. The dimensionality of this problem is $D = 47$.\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class CT18PDFs : \n", " def __init__(self) : \n", " self.parameters = {\n", " \"uv_0\" : 3.385, \"uv_1\" : 0.763, \"uv_2\" : 3.036, \"uv_3\" : 1.502, \"uv_4\" : -0.147,\"uv_5\" : 1.671, \"uv_6\" : 0.,\n", " \"dv_0\" : 0.490, \"dv_1\" : 0.763, \"dv_2\" : 3.036, \"dv_3\" : 2.615, \"dv_4\" : 1.828,\"dv_5\" : 2.721, \"dv_6\" : 0., \n", " \"g_0\" : 2.690, \"g_1\" : 0.531, \"g_2\" : 3.148, \"g_3\" : 3.032, \"g_4\" : -1.705, \"g_5\" : 1.354, \n", " \"ubar_0\" : 0.414, \"ubar_1\" : -0.022, \"ubar_2\" : 7.737, \"ubar_3\" : 4.0, \"ubar_4\" : 0.618,\"ubar_5\" : 0.195, \"ubar_6\" : 0.871, \"ubar_7\" : 0.267,\"ubar_8\" : 0.733,\n", " \"dbar_0\" : 0.414, \"dbar_1\" : -0.022, \"dbar_2\" : 7.737, \"dbar_3\" : 4.0, \"dbar_4\" : 0.292,\"dbar_5\" : 0.647, \"dbar_6\" : 0.474, \"dbar_7\" : 0.741,\"dbar_8\" :1.0,\n", " \"s_0\" : 0.288, \"s_1\" : -0.022, \"s_2\" : 10.31, \"s_3\" : 4.0, \"s_4\" : 0.466,\"s_5\" : 0.466, \"s_6\" : 0.225, \"s_7\" : 0.225,\"s_8\" : 1.0,\n", " }\n", " self.xlist = np.linspace(1e-3, 0.8, 50)\n", " self.paramNames = list(self.parameters.keys())\n", " self.originalData = self.getData()\n", "\n", " def uv(self, x) : \n", " a0 = self.parameters[\"uv_0\"]\n", " a1 = self.parameters[\"uv_1\"]\n", " a2 = self.parameters[\"uv_2\"]\n", " a3 = self.parameters[\"uv_3\"]\n", " a4 = self.parameters[\"uv_4\"]\n", " a5 = self.parameters[\"uv_5\"]\n", " a6 = self.parameters[\"uv_6\"]\n", " y= np.sqrt(x)\n", " P = np.sinh(a3)*(1-y)**4 + np.sinh(a4) *4*y*(1-y)**3 + np.sinh(a5) *6*y**2*(1-y)**2 + np.sinh(a6) *4*y**3*(1-y) + y**4\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def dv(self, x) : \n", " a0 = self.parameters[\"dv_0\"]\n", " a1 = self.parameters[\"dv_1\"]\n", " a2 = self.parameters[\"dv_2\"]\n", " a3 = self.parameters[\"dv_3\"]\n", " a4 = self.parameters[\"dv_4\"]\n", " a5 = self.parameters[\"dv_5\"]\n", " a6 = self.parameters[\"dv_6\"]\n", " y= np.sqrt(x)\n", " P = np.sinh(a3)*(1-y)**4 + np.sinh(a4) *4*y*(1-y)**3 + np.sinh(a5) *6*y**2*(1-y)**2 + np.sinh(a6) *4*y**3*(1-y) + y**4\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def g(self, x) : \n", " a0 = self.parameters[\"g_0\"]\n", " a1 = self.parameters[\"g_1\"]\n", " a2 = self.parameters[\"g_2\"]\n", " a3 = self.parameters[\"g_3\"]\n", " a4 = self.parameters[\"g_4\"]\n", " a5 = self.parameters[\"g_5\"]\n", " y= np.sqrt(x)\n", " P = np.sinh(a3)*(1-y)**3 + np.sinh(a4) *3*y*(1-y)**2 + np.sinh(a5) *3*y**2*(1-y) + y**3\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def ubar(self, x) : \n", " a0 = self.parameters[\"ubar_0\"]\n", " a1 = self.parameters[\"ubar_1\"]\n", " a2 = self.parameters[\"ubar_2\"]\n", " a3 = self.parameters[\"ubar_3\"]\n", " a4 = self.parameters[\"ubar_4\"]\n", " a5 = self.parameters[\"ubar_5\"]\n", " a6 = self.parameters[\"ubar_6\"]\n", " a7 = self.parameters[\"ubar_7\"]\n", " a8 = self.parameters[\"ubar_8\"]\n", " y= 1-(1-np.sqrt(x))**a3\n", " P = (1-y)**5 + a4 * 5*y*(1-y)**4 + a5 * 10*y**2*(1-y)**3 + a6 * 10*y**3*(1-y)**2 + a7 * 5*y**4*(1-y)+ a8 * 5*y**5\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def dbar(self, x) : \n", " a0 = self.parameters[\"dbar_0\"]\n", " a1 = self.parameters[\"dbar_1\"]\n", " a2 = self.parameters[\"dbar_2\"]\n", " a3 = self.parameters[\"dbar_3\"]\n", " a4 = self.parameters[\"dbar_4\"]\n", " a5 = self.parameters[\"dbar_5\"]\n", " a6 = self.parameters[\"dbar_6\"]\n", " a7 = self.parameters[\"dbar_7\"]\n", " a8 = self.parameters[\"dbar_8\"]\n", " y= 1-(1-np.sqrt(x))**a3\n", " P = (1-y)**5 + a4 * 5*y*(1-y)**4 + a5 * 10*y**2*(1-y)**3 + a6 * 10*y**3*(1-y)**2 + a7 * 5*y**4*(1-y)+ a8 * 5*y**5\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def s(self, x) : \n", " a0 = self.parameters[\"s_0\"]\n", " a1 = self.parameters[\"s_1\"]\n", " a2 = self.parameters[\"s_2\"]\n", " a3 = self.parameters[\"s_3\"]\n", " a4 = self.parameters[\"s_4\"]\n", " a5 = self.parameters[\"s_5\"]\n", " a6 = self.parameters[\"s_6\"]\n", " a7 = self.parameters[\"s_7\"]\n", " a8 = self.parameters[\"s_8\"]\n", " y= 1-(1-np.sqrt(x))**a3\n", " P = (1-y)**5 + a4 * 5*y*(1-y)**4 + a5 * 10*y**2*(1-y)**3 + a6 * 10*y**3*(1-y)**2 + a7 * 5*y**4*(1-y)+ a8 * 5*y**5\n", " return a0 * x**(a1-1)*(1-x)**a2*P\n", " \n", " def u(self, x) : return self.uv(x)+self.ubar(x)\n", " def d(self, x) : return self.dv(x)+self.dbar(x) \n", "\n", " def setParameter(self, pars) : \n", " assert(len(pars)==len(self.parameters)) \n", " self.parameters= dict(zip(self.paramNames, pars))\n", "\n", " def getData(self) : \n", " x= self.xlist\n", " return [ x*self.u(self.xlist), x*self.ubar(self.xlist), x*self.d(self.xlist), x*self.dbar(self.xlist), x*self.g(self.xlist), x*self.s(self.xlist)]\n", " \n", " def objective_function(self, params): \n", " self.setParameter(params) \n", " data = self.getData() \n", " ret =0\n", " for do, d in zip(self.originalData, data) : \n", " ret = ret + np.sum((do-d)**2)\n", " return ret" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ct18 = CT18PDFs()\n", "data = ct18.getData()\n", "# Create a 3x2 grid of subplots\n", "fig, axes = plt.subplots(2, 3, figsize=(10, 5))\n", "\n", "# List of labels for each plot\n", "labels = [\"u\", \"ubar\", \"d\", \"dbar\", \"g\", \"s\"]\n", "\n", "# Plotting each function in its corresponding subplot\n", "for i, ax in enumerate(axes.flatten()):\n", " ax.plot(ct18.xlist, data[i], label=labels[i])\n", " ax.set_xlim(0.1, 0.8)\n", " ax.set_xlabel(\"x\")\n", " if (i%3 == 0):\n", " ax.set_ylabel(r\"$xf(x)$\")\n", " ax.legend()\n", "plt.subplots_adjust(hspace=0.15, wspace=0.17)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dimension: 47\n", "\n", "Optimization Results:\n", "============================================================\n", "DE : f(x) = 1.1915898 \n", "ABC : f(x) = 18.48863 \n", "LSHADE : f(x) = 2.4194843 \n", "LSHADE_cnEpSin : f(x) = 0.53502555 \n", "JADE : f(x) = 2.9865776 \n", "jSO : f(x) = 1.6595643 \n", "j2020 : f(x) = 7.7543685 \n", "LSRTDE : f(x) = 0.28161899 \n", "NLSHADE_RSP : f(x) = 1.5008192 \n", "ARRDE : f(x) = 0.014022732 \n", "GWO_DE : f(x) = 1.4231015 \n", "NelderMead : f(x) = 0.072777402 \n", "DA : f(x) = 2.125748 \n", "L_BFGS_B : f(x) = 0.016089457 \n", "PSO : f(x) = 0.079717617 \n", "DMSPSO : f(x) = 0.0073668314 \n", "SPSO2011 : f(x) = 5.2242459 \n", "CMAES : f(x) = 1.9005412 \n", "BIPOP_aCMAES : f(x) = 0.66072312 \n", "Scipy L-BFGS-B : f(x) = 0.016125148 \n", "Scipy Dual Annealing : f(x) = 0.87696814 \n", "Scipy Nelder-Mead : f(x) = 0.072777402 \n", "============================================================\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Step 1: Define Problem Dimensions and Settings\n", "dimension = 47\n", "print(\"Dimension:\", dimension)\n", "\n", "bounds = [(-10, 10)] * dimension\n", "Nmaxeval = 100000\n", "x0 = [[1.0] * dimension] # Initial guess\n", "\n", "# Step 2: Set Up Parallel Execution for CT18PDFs\n", "t_parallel = mpy.Thread_Parallel(4, CT18PDFs) # Vectorize using 4 threads\n", "\n", "# Step 3: Define Optimization Algorithms\n", "algos = [\n", " \"DE\", \"ABC\", \"LSHADE\", \"LSHADE_cnEpSin\", \"JADE\", \"jSO\", \"j2020\", \"LSRTDE\", \"NLSHADE_RSP\",\n", " \"ARRDE\", \"GWO_DE\", \"NelderMead\", \"DA\", \"L_BFGS_B\", \"PSO\", \"DMSPSO\", \"SPSO2011\", \"CMAES\", \"BIPOP_aCMAES\"\n", "]\n", "\n", "# Step 4: Run Optimization and Collect Results\n", "results = {}\n", "\n", "print(\"\\nOptimization Results:\")\n", "print(\"=\" * 60)\n", "\n", "for algo in algos:\n", " res = mpy.Minimizer(\n", " t_parallel, bounds, x0=x0, algo=algo, relTol=0.0,\n", " maxevals=Nmaxeval, callback=None, seed=None,\n", " options={\n", " \"population_size\": 0,\n", " \"func_noise_ratio\" : 0.0, \n", " \"N_points_derivative\": 1\n", " }\n", " ).optimize()\n", "\n", " print(f\"{algo:<30}: f(x) = {res.fun:<20.8g}\")\n", " results[algo] = res\n", "\n", "# Step 5: Run SciPy Optimizers\n", "scipy_algorithms = [\n", " (\"Scipy L-BFGS-B\", minimize, {\"x0\": x0[0], \"bounds\": bounds, \"method\": \"L-BFGS-B\", \"options\": {\"maxfun\": Nmaxeval}}),\n", " (\"Scipy Dual Annealing\", dual_annealing, {\"x0\": x0[0],\"bounds\": bounds, \"maxfun\": Nmaxeval, \"no_local_search\": False}),\n", " (\"Scipy Nelder-Mead\", minimize, {\"x0\": x0[0], \"bounds\": bounds,\"method\": \"Nelder-Mead\", \"options\": {\"maxfev\": Nmaxeval, \"adaptive\": True}}),\n", "]\n", "\n", "for name, func_opt, kwargs in scipy_algorithms:\n", " res = func_opt(ct18.objective_function, **kwargs)\n", " print(f\"{name:<30}: f(x) = {res.fun:<20.8g}\")\n", " results[name] = res\n", "\n", "print(\"=\" * 60)\n", "\n", "# Step 6: Plot Results\n", "fig, axes = plt.subplots(2, 3, figsize=(12, 6))\n", "labels = [\"u-data\", \"ubar-data\", \"d-data\", \"dbar-data\", \"g-data\", \"s-data\"]\n", "\n", "# Plot each dataset in its corresponding subplot\n", "for i, ax in enumerate(axes.flatten()):\n", " ax.plot(ct18.xlist, data[i], label=labels[i], color=\"black\", linestyle=\"dotted\", linewidth=1.2)\n", "\n", " # Overlay model predictions for selected algorithms\n", " for algo in [\"ARRDE\", \"DA\", \"Scipy Dual Annealing\", \"L_BFGS_B\"]:\n", " ct18.setParameter(results[algo].x)\n", " theo = ct18.getData()\n", " ax.plot(ct18.xlist, theo[i], label=algo, linewidth=1.2, alpha=0.8)\n", "\n", " ax.set_xlim(0.1, 0.8)\n", " ax.set_xlabel(\"x\")\n", " if i % 3 == 0:\n", " ax.set_ylabel(r\"$xf(x)$\")\n", " ax.legend(fontsize=9)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }