【智能优化算法】遗传算法实数编码实现Python代码.zip

import numpy as np import random import matplotlib.pyplot as plt #目标函数 def function(x): y = 0 for i in range(len(x)): y = y + (x[i]**2 - 10 * np.cos(2 * np.pi * x[i]) + 10 ) return y #选择函数，三个参数：种群个体、种群数量、适应度值 def select(population,population_size,fitness): #fitness_proportion用来保存每个个体的选择概率 fitness_proportion = [] #计算适应度之和 fitness_sum = 0 for i in range(population_size): fitness_sum += fitness[i] #计算每个个体的选择概率 for i in range(population_size): fitness_proportion.append(fitness[i] / fitness_sum) #pie_fitness保存每个个体的累计概率 pie_fitness = [] cumsum = 0.0 for i in range(population_size): pie_fitness.append(cumsum + fitness_proportion[i]) #pie_fitness为由1到i个个体相加的适应度和组成的list cumsum += fitness_proportion[i] #所有个体生存率之和 # 生成随机数在轮盘上选点[0, 1) random_selection = [] for i in range(population_size): random_selection.append(random.random()) #返回随机生成的一个实数，它在[0,1)范围内。 # 选择新种群 new_population = [] random_selection_id = 0 while random_selection_id < population_size: #随机数处于个体对应的累计区间时，则将这个个体赋给新种群 for i in range(population_size): if random_selection[random_selection_id] < pie_fitness[i]: new_population.append(population[i]) break random_selection_id += 1 population = new_population return population #交叉函数，四个参数：种群个体、种群数量、交叉概率、维度信息 def crosscover(population,population_size,pc,dimen): for i in range(0, population_size - 1, 2): #每两个一对 #如果生成的随机数小于交叉概率 if random.random() < pc: # 随机选择交叉点,random.randint(0, dimen-1),返回[0,dimen-1]之间的整数 change_point = random.randint(0, dimen-1) temp1 = [] temp2 = [] #两个个体在交叉点进行交换 temp1.extend(population[i][0: change_point]) temp1.extend(population[i + 1][change_point:]) temp2.extend(population[i + 1][0: change_point]) temp2.extend(population[i][change_point:]) population[i] = temp1 population[i + 1] = temp2 population = np.array(population) return population #变异函数，四个参数：种群个体、种群数量、变异概率、维度信息 def mutation(population,population_size,pm,dimen): for i in range(population_size): #如果随机生成的数小于变异概率 if random.random() < pm: #随机生成个体中需要进行变异的数的数量mutation_num mutation_num = random.randint(0, dimen-1) #随机选择个体中mutation_num个数进行重新赋值 for j in range(mutation_num): mutation_point = random.randint(0, dimen-1) population[i][mutation_point] = np.random.uniform(low=-50, high=50) population = np.array(population) return population #解的取值范围 rangepop=[-50,50] #种群数量 pn = 50 #迭代次数 iterators = 1000 #交叉概率 pc = 0.6 #变异概率 pm = 0.05 #解的维度信息 dimen = 20 #种群，为数组形式 pop = np.zeros((pn,dimen)) #种群个体适应度值，为数组形式 fitness=np.zeros(pn) #随机初始化种群 for j in range(pn): pop[j] = np.random.uniform(low=-50, high=50,size=(1, dimen)) #计算适应度值 fitness[j] = function(pop[j]) #获取当前最优解bestpop和最优适应度值bestfit bestpop, bestfit = pop[fitness.argmax()].copy(), fitness.max() #bestfitness保存每次迭代中的最优适应度值，为数组形式 bestfitness=np.zeros(iterators) #开始迭代训练 for i in range(iterators): #选择操作 parents = select(pop,pn,fitness) #交叉操作 crossover1 = crosscover(parents,pn,pc,dimen) #变异操作 pop = mutation(crossover1,pn,pm,dimen) #将上一次迭代的最优适应度值和新的适应度值比较，选择更大的适应度值作为新的最优适应度值，对应的个体作为当前最优解 for j in range(pn): #确保个体取值在取值范围内[-50,50]内 pop[pop < rangepop[0]] = rangepop[0] pop[pop > rangepop[1]] = rangepop[1] #计算新个体适应度值 fitness[j] = function(pop[j]) #比较最优适应度值 if fitness[j] > bestfit: bestfit = fitness[j] bestpop = pop[j] bestfitness[i] = bestfit print("当前迭代次数:", i+1) print("最优适应度值是:", bestfitness[i]) print("迭代结束后:") print("最优解是:", bestpop) print("最优适应度值是:", bestfitness[-1]) fig=plt.figure(figsize=(15, 10), dpi=300) plt.title('The Change of Best Fitness',fontdict={'weight':'normal','size': 30}) x=range(1,1001,1) plt.plot(x,bestfitness,color="red",linewidth=3.0, linestyle="-") plt.tick_params(labelsize=25) plt.xlim(0,1001) plt.ylim(20000,50000) plt.xlabel("Epoch",fontdict={'weight':'normal','size': 30}) plt.ylabel("Fitness value",fontdict={'weight':'normal','size': 30}) plt.xticks(range(0,1001,100)) plt.yticks(range(20000,50000,5000)) plt.savefig("GA.png") plt.show()