HS.zip_common9j1_python 优化算法_syllablebgz_和声搜索算法_和声算法

# -*- coding: utf-8 -*- import numpy as np from HSIndividual import HSIndividual import random import copy import math import matplotlib.pyplot as plt import os 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 range(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 range(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 range(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 with open('test.txt', 'a') as f: f.write("Generation %d: optimal function value is: %f; average function value is %f \n" % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1])) ''' 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 fname = 'text.txt' f = open(fname, 'a+') newline= "Generation %d: optimal function value is: %f; average function value is %f " % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1]) f.write(newline+'\n') print(newline) ''' 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 newline = "Generation %d: optimal function value is: %f; average function value is %f" % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1]) f.write(newline + '\n') print("Generation %d: optimal function value is: %f; average function value is %f\n" % ( 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) f.write("Optimal function value is: %f; \n" % self.trace[self.t, 0]) f.write("Optimal solution is:\n ") f.write(np.fromstring(self.best.chrom, dtype=int)) f.close() 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() if __name__ == "__main__": bound = np.tile([[-600], [600]], 25) hs = HarmonySearch(60, 25, bound, 50, [0.9950, 0.4]) hs.solve()