Pm = 0.01 # mutation probability(变异概率)
Results = [] # save each generation's best solution(存储每一代的最优解，N个二元组)
Fit_value = [] # individual(个体适应度)
Fit_mean = [] # average(平均适应度)
num_users = 2 # number of users
const_load = [] # former user schedule load
for u in range(num_users):
# run genetic algorithm for one user
Pop = geneEncoding(Species_size, Pop_size,
Chrom_length) #construct ecosphere(建立生物圈)
for i in range(Iteration): #begin genetic algorithm(开始进行遗传算法)
Temp_pop_start, Temp_pop_end = calobjValue(
Pop, Chrom_length, Timelist,
Species_size) # calculate power supply time period(计算供电时间)
# TODO: Redefine calcostValue function based on const_load
Cost_list = calcostValue(
Temp_pop_start, Temp_pop_end, u,
const_load) #get the electricity price(得到电费价格表)
Best_gene, Best_Results, Best_starttime, Best_endtime = best(
Pop, Cost_list, Temp_pop_start, Temp_pop_end
) #get the best gene and the corresponding power start time and end time(找到最优基因及最优解以及最优解对应的供电起始时间)
Results.append([
Best_Results, Best_starttime, Best_endtime
]) #save the best result of each generation(存储每一代的最优结果)
selection(Pop, Cost_list) #copy the new species(新种群开始进行复制)
crossover(Pop, Pc) #(对新种群进行交配)
mutation(Pop, Pm) #对种群进行变异
pop_size = 1000 # 世代数
max_value = 15 # 遺伝子の最大値
chrom_length = 4 # バイナリシーケンスの長さ
pc = 0.6 # 交叉確率
pm = 0.01 # 突然変異確率
max_results = [[]] # 世代ごとの最大解を保存
min_results = [[]] # 世代ごとの最小解を保存
fit_value = [] # 個体適応度
fit_mean = [] # 平均適応度
# pop = [[0, 1, 0, 1] for i in range(pop_size)]
pop = geneEncoding(pop_size, chrom_length)
for i in range(pop_size):
obj_value = calobjValue(pop, chrom_length, max_value) # 個体評価
max_indi1, max_indi2, max_indi3, max_fit, min_indi1, min_indi2, min_indi3, min_fit = best(pop, obj_value)
max_results.append([max_fit, b2d(max_indi1, max_value, chrom_length), b2d(max_indi2, max_value, chrom_length), b2d(max_indi3, max_value, chrom_length)])
min_results.append([min_fit, b2d(min_indi1, max_value, chrom_length), b2d(min_indi2, max_value, chrom_length), b2d(min_indi3, max_value, chrom_length)])
selection(pop, obj_value) # 新しい個体群をコピー
crossover(pop, pc) # 交叉
mutation(pop, pm) # 突然変異
max_results = max_results[1:]
max_results.sort()
min_results = min_results[1:]
min_results.sort(reverse=True)
print(max_results[-1])
print(min_results[-1])
print("max : y = %f, x1 = %f, x2 = %f, x3 = %f" % (max_results[-1][0], max_results[-1][1], max_results[-1][2], max_results[-1][3]))
pm = 0.01 # 变异概率
results = [[]] # 存储每一代的最优解，[[]]表示是二维数组。
fit_value = [] # 个体适应度
simuNodesIndex, nodesIndexs = readSimulationNodes(inpPath)
#产生初始种群
pop = geneEncoding(
pop_size, chrom_length
) #pop中的个体原始为[421, 76, 213, 632, 1011, 102, 1289, 334,.......]代表其节点索引位置80个pop形状500*80
for i in range(500): #共迭代500代
# 对每一代做计算，判断优劣,获取当代最好个体
start = time.clock()
print('开始计算第%s代的适应度' % i)
fit_value = calobjValue(
pop, Residualsmatrix,
SensitivityMatrix) # 个体评价（适应度函数），obj_value放置对应每个个体的适应度值
end1 = time.clock()
print('完成适应度计算耗时%s' % (end1 - start), '适应度:', fit_value)
best_individual, best_fit = best(
pop, fit_value) # 第一个存储最优的解对应的整数编码,第二个存储最优基因对应的适应度，值越小越好
# end2 = time.clock()
# print('完成挑选最好的耗时%s'%(end2-end1))
print('最好的', best_fit)
print('最好的个体', geneDecode(best_individual, nodesIndexs))
results.append([best_fit,
geneDecode(best_individual,
nodesIndexs)]) ## 存储每一代的最优解,及其最终的节点ID,值越小越好
# 遗传算法核心，生成种群，为下一次计算适应度做准备
x_ = x_change(-5, 5) #x的取值范围
max_value = (x_[1] - x_[0]) # 基因中允许出现的最大值
pop_size = 500 # 种群数量
chrom_length = 10 # 染色体长度
N = 200 #迭代次数
pc = 0.6 # 交配概率
pm = 0.01 # 变异概率
results = [[]] # 存储每一代的最优解，N个二元组
fit_value = [] # 个体适应度
fit_mean = [] # 平均适应度
# pop = [[0, 1, 0, 1, 0, 1, 0, 1, 0, 1] for i in range(pop_size)]
pop = geneEncoding(pop_size, chrom_length)
for i in range(N):
obj_value = calobjValue(pop, chrom_length, max_value, x_[0]) # 个体评价
fit_value = calfitValue(obj_value) # 淘汰
best_individual, best_fit = best(pop, fit_value) # 第一个存储最优的解, 第二个存储最优基因
results.append([best_fit, b2d(best_individual, max_value, chrom_length)])
selection(pop, fit_value) # 新种群复制
crossover(pop, pc) # 交配
mutation(pop, pm) # 变异
results = results[1:]
results.sort()
print(results[-1])
print(best_individual)
print(best_fit)
print(obj_value[1])