HSIndividual.py
import numpy as np
import ObjFunction class HSIndividual: '''
individual of harmony search algorithm
''' def __init__(self, vardim, bound):
'''
vardim: dimension of variables
bound: boundaries of variables
'''
self.vardim = vardim
self.bound = bound
self.fitness = 0. def generate(self):
'''
generate a random chromsome for harmony search algorithm
'''
len = self.vardim
rnd = np.random.random(size=len)
self.chrom = np.zeros(len)
for i in xrange(0, len):
self.chrom[i] = self.bound[0, i] + \
(self.bound[1, i] - self.bound[0, i]) * rnd[i] def calculateFitness(self):
'''
calculate the fitness of the chromsome
'''
self.fitness = ObjFunction.GrieFunc(
self.vardim, self.chrom, self.bound)
HS.py
import numpy as np
from HSIndividual import HSIndividual
import random
import copy
import math
import matplotlib.pyplot as plt class HarmonySearch: '''
the class for harmony search algorithm
''' def __init__(self, sizepop, vardim, bound, MAXGEN, params):
'''
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[HMCR, PAR]
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.MAXGEN = MAXGEN
self.params = params
self.population = []
self.fitness = np.zeros((self.sizepop, 1))
self.trace = np.zeros((self.MAXGEN, 2)) def initialize(self):
'''
initialize the population of hs
'''
for i in xrange(0, self.sizepop):
ind = HSIndividual(self.vardim, self.bound)
ind.generate()
self.population.append(ind) def evaluation(self):
'''
evaluation the fitness of the population
'''
for i in xrange(0, self.sizepop):
self.population[i].calculateFitness()
self.fitness[i] = self.population[i].fitness def improvise(self):
'''
improvise a new harmony
'''
ind = HSIndividual(self.vardim, self.bound)
ind.chrom = np.zeros(self.vardim)
for i in xrange(0, self.vardim):
if random.random() < self.params[0]:
if random.random() < self.params[1]:
ind.chrom[i] += self.best.chrom[i]
else:
worstIdx = np.argmin(self.fitness)
xr = 2 * self.best.chrom[i] - \
self.population[worstIdx].chrom[i]
if xr < self.bound[0, i]:
xr = self.bound[0, i]
if xr > self.bound[1, i]:
xr = self.bound[1, i]
ind.chrom[i] = self.population[worstIdx].chrom[
i] + (xr - self.population[worstIdx].chrom[i]) * random.random()
else:
ind.chrom[i] = self.bound[
0, i] + (self.bound[1, i] - self.bound[0, i]) * random.random()
ind.calculateFitness()
return ind def update(self, ind):
'''
update harmony memory
'''
minIdx = np.argmin(self.fitness)
if ind.fitness > self.population[minIdx].fitness:
self.population[minIdx] = ind
self.fitness[minIdx] = ind.fitness def solve(self):
'''
the evolution process of the hs algorithm
'''
self.t = 0
self.initialize()
self.evaluation()
best = np.max(self.fitness)
bestIndex = np.argmax(self.fitness)
self.best = copy.deepcopy(self.population[bestIndex])
self.avefitness = np.mean(self.fitness)
self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
print("Generation %d: optimal function value is: %f; average function value is %f" % (
self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
while self.t < self.MAXGEN - 1:
self.t += 1
ind = self.improvise()
self.update(ind)
best = np.max(self.fitness)
bestIndex = np.argmax(self.fitness)
if best > self.best.fitness:
self.best = copy.deepcopy(self.population[bestIndex])
self.avefitness = np.mean(self.fitness)
self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
print("Generation %d: optimal function value is: %f; average function value is %f" % (
self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
print("Optimal function value is: %f; " % self.trace[self.t, 0])
print "Optimal solution is:"
print self.best.chrom
self.printResult() def printResult(self):
'''
plot the result of abs algorithm
'''
x = np.arange(0, self.MAXGEN)
y1 = self.trace[:, 0]
y2 = self.trace[:, 1]
plt.plot(x, y1, 'r', label='optimal value')
plt.plot(x, y2, 'g', label='average value')
plt.xlabel("Iteration")
plt.ylabel("function value")
plt.title("Harmony search algorithm for function optimization")
plt.legend()
plt.show()
运行程序：
if __name__ == "__main__":
bound = np.tile([[-600], [600]], 25)
hs = HS(60, 25, bound, 5000, [0.9950, 0.4])
hs.solve()
ObjFunction见简单遗传算法-python实现。