103 lines
2.6 KiB
Python
103 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Sun May 15 22:37:00 2016
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@author: Hossam Faris
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"""
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import random
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import numpy
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from solution import solution
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import time
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def PSO(objf, lb, ub, dim, PopSize, iters):
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# PSO parameters
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Vmax = 6
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wMax = 0.9
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wMin = 0.2
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c1 = 2
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c2 = 2
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s = solution()
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if not isinstance(lb, list):
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lb = [lb] * dim
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if not isinstance(ub, list):
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ub = [ub] * dim
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######################## Initializations
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vel = numpy.zeros((PopSize, dim))
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pBestScore = numpy.zeros(PopSize)
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pBestScore.fill(float("inf"))
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pBest = numpy.zeros((PopSize, dim))
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gBest = numpy.zeros(dim)
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gBestScore = float("inf")
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pos = numpy.zeros((PopSize, dim))
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for i in range(dim):
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pos[:, i] = numpy.random.uniform(0, 1, PopSize) * (ub[i] - lb[i]) + lb[i]
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convergence_curve = numpy.zeros(iters)
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############################################
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print('PSO is optimizing "' + objf.__name__ + '"')
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timerStart = time.time()
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s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
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for l in range(0, iters):
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for i in range(0, PopSize):
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# pos[i,:]=checkBounds(pos[i,:],lb,ub)
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for j in range(dim):
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pos[i, j] = numpy.clip(pos[i, j], lb[j], ub[j])
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# Calculate objective function for each particle
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fitness = objf(pos[i, :])
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if pBestScore[i] > fitness:
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pBestScore[i] = fitness
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pBest[i, :] = pos[i, :].copy()
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if gBestScore > fitness:
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gBestScore = fitness
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gBest = pos[i, :].copy()
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# Update the W of PSO
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w = wMax - l * ((wMax - wMin) / iters)
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for i in range(0, PopSize):
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for j in range(0, dim):
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r1 = random.random()
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r2 = random.random()
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vel[i, j] = (
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w * vel[i, j]
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+ c1 * r1 * (pBest[i, j] - pos[i, j])
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+ c2 * r2 * (gBest[j] - pos[i, j])
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)
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if vel[i, j] > Vmax:
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vel[i, j] = Vmax
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if vel[i, j] < -Vmax:
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vel[i, j] = -Vmax
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pos[i, j] = pos[i, j] + vel[i, j]
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convergence_curve[l] = gBestScore
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if l % 1 == 0:
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print(["At iteration " + str(l + 1) + " the best fitness is " + str(gBestScore)])
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timerEnd = time.time()
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s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
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s.executionTime = timerEnd - timerStart
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s.convergence = convergence_curve
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s.optimizer = "PSO"
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s.bestIndividual = gBest
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s.objfname = objf.__name__
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return s
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