-python implementation of particle swarm optimization algorithm

psoindividual.py

1 ImportNumPy as NP2 Importobjfunction3 ImportCopy4 5 6 classpsoindividual:7 8     " "9 Individual of PSOTen     " " One  A     def __init__(self, Vardim, bound): -         " " - Vardim:dimension of Variables the Bound:boundaries of Variables -         " " -Self.vardim =Vardim -Self.bound =bound +Self.fitness =0. -  +     defGenerate (self): A         " " at generate a Rondom chromsome -         " " -Len =Self.vardim -Rnd = Np.random.random (size=len) -Self.chrom =Np.zeros (len) -self.velocity = Np.random.random (size=len) in          forIinchxrange (0, Len): -Self.chrom[i] = self.bound[0, I] +  to(Self.bound[1, I]-self.bound[0, I]) *Rnd[i] +Self.bestposition =Np.zeros (len) -Self.bestfitness =0. the  *     defcalculatefitness (self): $         " "Panax Notoginseng calculate the fitness of the Chromsome -         " " theSelf.fitness =Objfunction.griefunc ( +Self.vardim, Self.chrom, Self.bound)

pso.py

1 ImportNumPy as NP2  fromPsoindividualImportpsoindividual3 ImportRandom4 ImportCopy5 ImportMatplotlib.pyplot as Plt6 7 8 classparticleswarmoptimization:9 Ten     " " One The class for particle Swarm optimization A     " " -  -     def __init__(self, sizepop, Vardim, bound, Maxgen, params): the         " " - sizepop:population Sizepop - Vardim:dimension of Variables - Bound:boundaries of Variables + maxgen:termination Condition - params:algorithm Required parameters, it is a list which is consisting of[w, C1, C2] +         " " ASelf.sizepop =Sizepop atSelf.vardim =Vardim -Self.bound =bound -Self. Maxgen =Maxgen -Self.params =params -Self.population = [] -Self.fitness = Np.zeros ((self.sizepop, 1)) inSelf.trace = Np.zeros (self. Maxgen, 2)) -  to     defInitialize (self): +         " " - Initialize the population of PSO the         " " *          forIinchxrange (0, Self.sizepop): $IND =psoindividual (Self.vardim, Self.bound)Panax Notoginseng ind.generate () - Self.population.append (Ind) the  +     defEvaluation (self): A         " " the Evaluation The fitness of the population +         " " -          forIinchxrange (0, Self.sizepop): $ self.population[i].calculatefitness () $Self.fitness[i] =self.population[i].fitness -             ifSelf.population[i].fitness >self.population[i].bestfitness: -Self.population[i].bestfitness =self.population[i].fitness theSelf.population[i].bestindex =Copy.deepcopy ( - self.population[i].chrom)Wuyi  the     defUpdate (self): -         " " Wu Update the population of PSO -         " " About          forIinchxrange (0, Self.sizepop): $self.population[i].velocity = self.params[0] * self.population[i].velocity + self.params[1] * Np.random.random ( Self.vardim) * ( -Self.population[i].bestposition-self.population[i].chrom) + self.params[2] * Np.random.random (Self.vardim) * ( Self.best.chrom-self.population[i].chrom) -Self.population[i].chrom =self.population[ -I].chrom +self.population[i].velocity A  +     defSolve (self): the         " " - The evolution process of the PSO algorithm $         " " theSELF.T =0 the self.initialize () the self.evaluation () theBest =Np.max (self.fitness) -Bestindex =Np.argmax (self.fitness) inSelf.best =copy.deepcopy (Self.population[bestindex]) theSelf.avefitness =Np.mean (self.fitness) theSELF.TRACE[SELF.T, 0] = (1-self.best.fitness)/self.best.fitness AboutSELF.TRACE[SELF.T, 1] = (1-self.avefitness)/self.avefitness the         Print("Generation%d:optimal function value is:%f, average function value is%f"% ( theSELF.T, self.trace[self.t, 0], self.trace[self.t, 1])) the          whileself.t < self. MAXGEN-1: +SELF.T + = 1 - self.update () the self.evaluation ()BayiBest =Np.max (self.fitness) theBestindex =Np.argmax (self.fitness) the             ifBest >self.best.fitness: -Self.best =copy.deepcopy (Self.population[bestindex]) -Self.avefitness =Np.mean (self.fitness) theSELF.TRACE[SELF.T, 0] = (1-self.best.fitness)/self.best.fitness theSELF.TRACE[SELF.T, 1] = (1-self.avefitness)/self.avefitness the             Print("Generation%d:optimal function value is:%f, average function value is%f"% ( theSELF.T, self.trace[self.t, 0], self.trace[self.t, 1])) -  the         Print("Optimal function value is:%f;"%self.trace[self.t, 0]) the         Print "Optimal solution is:" the         PrintSelf.best.chrom94 Self.printresult () the  the     defPrintresult (self): the         " "98 plot The result of the PSO algorithm About         " " -x =np.arange (0, self. Maxgen)101Y1 =self.trace[:, 0]102y2 = self.trace[:, 1]103Plt.plot (x, y1,'R', label='Optimal Value')104Plt.plot (x, Y2,'g', label='Average value') thePlt.xlabel ("Iteration")106Plt.ylabel ("function Value")107Plt.title ("particle Swarm optimization algorithm for function optimization")108 plt.legend ()109Plt.show ()

To run the program:

1 if __name__ " __main__ " : 2 3     bound = Np.tile ([[ -600], [+]],4     PSO = PSO (max, bound, +, [0.7298, 1.4962, 1.4962
   
    ])
    5     pso.solve ()
   

Objfunction See simple genetic algorithm-python realization.

-python implementation of particle swarm optimization algorithm