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"""Particle Swarm Optimization algorithm. |
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Date: 12. 2. 2018 |
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Authors : Uros Mlakar |
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License: MIT |
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Reference paper: Kennedy, J. and Eberhart, R. "Particle Swarm Optimization". |
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Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942--1948, 1995. |
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EDITED: TODO: Tests and validation! Bug in code. |
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""" |
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import random as rnd |
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import copy |
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__all__ = ['ParticleSwarmAlgorithm'] |
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class Particle(object): |
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"""Defines particle for population.""" |
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# pylint: disable=too-many-instance-attributes |
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def __init__(self, D, LB, UB, vMin, vMax): |
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self.D = D |
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self.LB = LB |
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self.UB = UB |
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self.vMin = vMin |
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self.vMax = vMax |
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self.Solution = [] |
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self.Velocity = [] |
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self.pBestPosition = [] |
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self.pBestSolution = [] |
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self.bestFitness = float('inf') |
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self.Fitness = float('inf') |
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self.generateParticle() |
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def generateParticle(self): |
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self.Solution = [self.LB + (self.UB - self.LB) * rnd.random() |
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for _i in range(self.D)] |
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self.Velocity = [0 for _i in range(self.D)] |
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self.pBestSolution = [0 for _i in range(self.D)] |
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self.bestFitness = float('inf') |
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def evaluate(self): |
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self.Fitness = Particle.FuncEval(self.D, self.Solution) |
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self.checkPersonalBest() |
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def checkPersonalBest(self): |
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if self.Fitness < self.bestFitness: |
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self.pBestSolution = self.Solution |
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self.bestFitness = self.Fitness |
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def simpleBound(self): |
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for i in range(self.D): |
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if self.Solution[i] < self.LB: |
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self.Solution[i] = self.LB |
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if self.Solution[i] > self.UB: |
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self.Solution[i] = self.UB |
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if self.Velocity[i] < self.vMin: |
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self.Velocity[i] = self.vMin |
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if self.Velocity[i] > self.vMax: |
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self.Velocity[i] = self.vMax |
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def toString(self): |
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pass |
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def __eq__(self, other): |
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return self.Solution == other.Solution and self.Fitness == other.Fitness |
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class ParticleSwarmAlgorithm(object): |
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# pylint: disable=too-many-instance-attributes |
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def __init__(self, Np, D, nFES, C1, C2, w, vMin, vMax, Lower, Upper, function): |
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"""Constructor.""" |
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self.Np = Np |
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self.D = D |
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self.nFES = nFES |
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self.C1 = C1 |
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self.C2 = C2 |
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self.w = w |
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self.vMin = vMin |
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self.vMax = vMax |
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self.Lower = Lower |
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self.Upper = Upper |
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self.Swarm = [] |
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Particle.FuncEval = staticmethod(function) |
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self.gBest = Particle(self.D, self.Lower, self.Upper, self.vMin, self.vMax) |
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def evalSwarm(self): |
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for p in self.Swarm: |
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p.evaluate() |
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if p.Fitness < self.gBest.Fitness: |
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self.gBest = copy.deepcopy(p) |
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def initSwarm(self): |
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for _i in range(self.Np): |
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self.Swarm.append(Particle(self.D, self.Lower, self.Upper, self.vMin, self.vMax)) |
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def moveSwarm(self, Swarm): |
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MovedSwarm = [] |
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for p in Swarm: |
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part1 = ([(a - b) * rnd.random() * self.C1 for a, |
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b in zip(p.pBestSolution, p.Solution)]) |
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part2 = ([(a - b) * rnd.random() * self.C2 for a, |
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b in zip(self.gBest.Solution, p.Solution)]) |
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p.Velocity = ([self.w * a + b + c for a, b, |
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c in zip(p.Velocity, part1, part2)]) |
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p.Solution = ([a + b for a, b in zip(p.Solution, p.Velocity)]) |
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p.simpleBound() |
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p.evaluate() |
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if p.Fitness < self.gBest.Fitness: |
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self.gBest = copy.deepcopy(p) |
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MovedSwarm.append(p) |
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return MovedSwarm |
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def run(self): |
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self.initSwarm() |
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self.evalSwarm() |
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FEs = self.Np |
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while FEs <= self.nFES: |
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MovedSwarm = self.moveSwarm(self.Swarm) |
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self.Swarm = MovedSwarm |
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FEs += self.Np |
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return self.gBest.Fitness |
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NiaOrg /
NiaPy
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