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MDAF/Sample codes/PSO.ipynb
Remi Ehounou 9658b2bfa0 adding jupyter notebooks
2021-03-27 18:25:25 -04:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\npso.py\\nA simple implementation of the Particle Swarm Optimisation Algorithm.\\nUses Numpy for matrix operations. \\nPradeep Gowda 2009-03-16\\n\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#!/usr/bin/env python\n",
"\n",
"'''\n",
"pso.py\n",
"A simple implementation of the Particle Swarm Optimisation Algorithm.\n",
"Uses Numpy for matrix operations. \n",
"Pradeep Gowda 2009-03-16\n",
"\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from numpy import *\n",
"from random import random\n",
"from math import sin, sqrt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"iter_max = 10000\n",
"pop_size = 100\n",
"dimensions = 2\n",
"c1 = 2\n",
"c2 = 2\n",
"err_crit = 0.00001"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class Particle:\n",
" pass\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def f6(param):\n",
" para = param*10\n",
" para = param[0:2]\n",
" num = (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) * \\\n",
" (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) - 0.5\n",
" denom = (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1]))) * \\\n",
" (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1])))\n",
" f6 = 0.5 - (num/denom)\n",
" errorf6 = 1 - f6\n",
" return f6, errorf6;\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#initialize the particles\n",
"particles = []\n",
"for i in range(pop_size):\n",
" p = Particle()\n",
" p.params = array([random() for i in range(dimensions)])\n",
" p.fitness = 0.0\n",
" p.v = 0.0\n",
" particles.append(p)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# let the first particle be the global best\n",
"gbest = particles[0]\n",
"err = 999999999\n",
"while i < iter_max :\n",
" for p in particles:\n",
" fitness,err = f6(p.params)\n",
" if fitness > p.fitness:\n",
" p.fitness = fitness\n",
" p.best = p.params\n",
"\n",
" if fitness > gbest.fitness:\n",
" gbest = p\n",
" v = p.v + c1 * random() * (p.best - p.params) \\\n",
" + c2 * random() * (gbest.params - p.params)\n",
" p.params = p.params + v\n",
" \n",
" i += 1\n",
" if err < err_crit:\n",
" break\n",
" #progress bar. '.' = 10%\n",
" if i % (iter_max/10) == 0:\n",
" print('.')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Particle Swarm Optimisation\n",
"\n",
"PARAMETERS\n",
" ---------\n",
"Population size : 100\n",
"Dimensions : 2\n",
"Error Criterion : 1e-05\n",
"c1 : 2\n",
"c2 : 2\n",
"function : f6\n",
"RESULTS\n",
" -------\n",
"gbest fitness : 0.9999998959199905\n",
"gbest params : [ 0.00025095 -0.00020248]\n",
"iterations : 107\n",
"params: [-0.00186491 -0.00125129], fitness: 0.9999940501855731, best: [-0.00202009 -0.00136496]\n",
"params: [-0.02024712 -0.03991588], fitness: 0.9921880164520516, best: [0.04062241 0.07857791]\n",
"params: [ 0.00164549 -0.00145206], fitness: 0.9999880309131435, best: [ 0.00230598 -0.00257675]\n",
"params: [0.03177491 0.02362014], fitness: 0.9977421176253256, best: [-0.0382471 -0.02818674]\n",
"params: [-0.00580889 -0.02935609], fitness: 0.9972713772514697, best: [-0.0104284 -0.0511824]\n",
"params: [ 0.00018095 -0.0006362 ], fitness: 0.9999995620667945, best: [ 0.00018095 -0.0006362 ]\n",
"params: [0.00271906 0.00529463], fitness: 0.9998854432411289, best: [-0.00382274 -0.00999167]\n",
"params: [ 0.00111366 -0.00196693], fitness: 0.9999894915409011, best: [-0.00203316 0.00252275]\n",
"params: [-0.28785638 -1.4943572 ], fitness: 0.995928521299837, best: [0.01303161 0.06247514]\n",
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"params: [ 0.00022169 -0.00020905], fitness: 0.9999993066270301, best: [ 8.29086178e-04 -7.27774663e-05]\n",
"params: [0.00031617 0.00011897], fitness: 0.9999979816879985, best: [ 3.94733810e-06 -1.41995827e-03]\n",
"params: [-0.01695506 0.05417627], fitness: 0.9995244025668919, best: [ 0.00419573 -0.02139144]\n",
"params: [-0.00081278 0.00472481], fitness: 0.9996436046229067, best: [ 0.00417984 -0.01840138]\n",
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"params: [ 0.00017354 -0.0010857 ], fitness: 0.9999993595611627, best: [ 0.00073676 -0.00031141]\n",
"params: [0.0205555 0.01924657], fitness: 0.9990619241474461, best: [-0.02188822 -0.02140885]\n",
"params: [ 0.00063561 -0.0029103 ], fitness: 0.9999993990164586, best: [ 0.00066814 -0.00039239]\n",
"params: [-0.02874995 -0.0130972 ], fitness: 0.9926590734245718, best: [-0.07822951 -0.03509736]\n",
"params: [ 1.11162910e-03 -4.46943752e-05], fitness: 0.9997728668820509, best: [0.01485916 0.00247564]\n",
"params: [-0.00055077 -0.0058934 ], fitness: 0.9998629794284519, best: [0.00190573 0.01154375]\n",
"params: [0.00396443 0.0142522 ], fitness: 0.9997489163752509, best: [0.00422452 0.01526458]\n",
"params: [0.00040531 0.00023427], fitness: 0.9999994074332612, best: [0.00052221 0.00056504]\n",
"params: [ 0.00015343 -0.00047534], fitness: 0.9999996332860702, best: [0.00046276 0.00039012]\n",
"params: [-0.0003435 -0.00159017], fitness: 0.9999905888674923, best: [0.0014865 0.0026818]\n"
]
}
],
"source": [
"print ('\\nParticle Swarm Optimisation\\n')\n",
"print ('PARAMETERS\\n','-'*9)\n",
"print ('Population size : ', pop_size)\n",
"print ('Dimensions : ', dimensions)\n",
"print ('Error Criterion : ', err_crit)\n",
"print ('c1 : ', c1)\n",
"print ('c2 : ', c2)\n",
"print ('function : f6')\n",
"\n",
"print ('RESULTS\\n', '-'*7)\n",
"print ('gbest fitness : ', gbest.fitness)\n",
"print ('gbest params : ', gbest.params)\n",
"print ('iterations : ', i+1)\n",
"## Uncomment to print particles\n",
"for p in particles:\n",
" print ('params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best) )\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"particles[7].v"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5.00111366, 4.99803307])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"particles[7].params+5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
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"nbformat_minor": 4
}
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