粒子群优化算法-python实现

 1 import numpy as np
 2 import ObjFunction
 3 import copy
 4 
 5 
 6 class PSOIndividual:
 7 
 8     ‘‘‘
 9     individual of PSO
10     ‘‘‘
11 
12     def __init__(self, vardim, bound):
13         ‘‘‘
14         vardim: dimension of variables
15         bound: boundaries of variables
16         ‘‘‘
17         self.vardim = vardim
18         self.bound = bound
19         self.fitness = 0.
20 
21     def generate(self):
22         ‘‘‘
23         generate a rondom chromsome
24         ‘‘‘
25         len = self.vardim
26         rnd = np.random.random(size=len)
27         self.chrom = np.zeros(len)
28         self.velocity = np.random.random(size=len)
29         for i in xrange(0, len):
30             self.chrom[i] = self.bound[0, i] + 31                 (self.bound[1, i] - self.bound[0, i]) * rnd[i]
32         self.bestPosition = np.zeros(len)
33         self.bestFitness = 0.
34 
35     def calculateFitness(self):
36         ‘‘‘
37         calculate the fitness of the chromsome
38         ‘‘‘
39         self.fitness = ObjFunction.GrieFunc(
40             self.vardim, self.chrom, self.bound)

  1 import numpy as np
  2 from PSOIndividual import PSOIndividual
  3 import random
  4 import copy
  5 import matplotlib.pyplot as plt
  6 
  7 
  8 class ParticleSwarmOptimization:
  9 
 10     ‘‘‘
 11     the class for Particle Swarm Optimization
 12     ‘‘‘
 13 
 14     def __init__(self, sizepop, vardim, bound, MAXGEN, params):
 15         ‘‘‘
 16         sizepop: population sizepop
 17         vardim: dimension of variables
 18         bound: boundaries of variables
 19         MAXGEN: termination condition
 20         params: algorithm required parameters, it is a list which is consisting of[w, c1, c2]
 21         ‘‘‘
 22         self.sizepop = sizepop
 23         self.vardim = vardim
 24         self.bound = bound
 25         self.MAXGEN = MAXGEN
 26         self.params = params
 27         self.population = []
 28         self.fitness = np.zeros((self.sizepop, 1))
 29         self.trace = np.zeros((self.MAXGEN, 2))
 30 
 31     def initialize(self):
 32         ‘‘‘
 33         initialize the population of pso
 34         ‘‘‘
 35         for i in xrange(0, self.sizepop):
 36             ind = PSOIndividual(self.vardim, self.bound)
 37             ind.generate()
 38             self.population.append(ind)
 39 
 40     def evaluation(self):
 41         ‘‘‘
 42         evaluation the fitness of the population
 43         ‘‘‘
 44         for i in xrange(0, self.sizepop):
 45             self.population[i].calculateFitness()
 46             self.fitness[i] = self.population[i].fitness
 47             if self.population[i].fitness > self.population[i].bestFitness:
 48                 self.population[i].bestFitness = self.population[i].fitness
 49                 self.population[i].bestIndex = copy.deepcopy(
 50                     self.population[i].chrom)
 51 
 52     def update(self):
 53         ‘‘‘
 54         update the population of pso
 55         ‘‘‘
 56         for i in xrange(0, self.sizepop):
 57             self.population[i].velocity = self.params[0] * self.population[i].velocity + self.params[1] * np.random.random(self.vardim) * (
 58                 self.population[i].bestPosition - self.population[i].chrom) + self.params[2] * np.random.random(self.vardim) * (self.best.chrom - self.population[i].chrom)
 59             self.population[i].chrom = self.population[
 60                 i].chrom + self.population[i].velocity
 61 
 62     def solve(self):
 63         ‘‘‘
 64         the evolution process of the pso algorithm
 65         ‘‘‘
 66         self.t = 0
 67         self.initialize()
 68         self.evaluation()
 69         best = np.max(self.fitness)
 70         bestIndex = np.argmax(self.fitness)
 71         self.best = copy.deepcopy(self.population[bestIndex])
 72         self.avefitness = np.mean(self.fitness)
 73         self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
 74         self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
 75         print("Generation %d: optimal function value is: %f; average function value is %f" % (
 76             self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
 77         while self.t < self.MAXGEN - 1:
 78             self.t += 1
 79             self.update()
 80             self.evaluation()
 81             best = np.max(self.fitness)
 82             bestIndex = np.argmax(self.fitness)
 83             if best > self.best.fitness:
 84                 self.best = copy.deepcopy(self.population[bestIndex])
 85             self.avefitness = np.mean(self.fitness)
 86             self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
 87             self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
 88             print("Generation %d: optimal function value is: %f; average function value is %f" % (
 89                 self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
 90 
 91         print("Optimal function value is: %f; " % self.trace[self.t, 0])
 92         print "Optimal solution is:"
 93         print self.best.chrom
 94         self.printResult()
 95 
 96     def printResult(self):
 97         ‘‘‘
 98         plot the result of pso algorithm
 99         ‘‘‘
100         x = np.arange(0, self.MAXGEN)
101         y1 = self.trace[:, 0]
102         y2 = self.trace[:, 1]
103         plt.plot(x, y1, ‘r‘, label=‘optimal value‘)
104         plt.plot(x, y2, ‘g‘, label=‘average value‘)
105         plt.xlabel("Iteration")
106         plt.ylabel("function value")
107         plt.title("Particle Swarm Optimization algorithm for function optimization")
108         plt.legend()
109         plt.show()

1 if __name__ == "__main__":
2 
3     bound = np.tile([[-600], [600]], 25)
4     pso = PSO(60, 25, bound, 1000, [0.7298, 1.4962, 1.4962])
5     pso.solve()