def iteration(self):
"""
算法迭代主程序
:return: 最后迭代完成的排序,去重的种群
"""
node_count = len(self.read_json())
pop = population(self.pop_size, self.individuals_num, node_count)
node_dirt = self.read_json()
func_obj = MinMax2(pop, node_dirt)
for i in range(self.iterations_num):
copy_pop = pop.copy()
selection(pop, func_obj)
crossover(pop, self.cross_probability)
mutation(pop, self.mutation_probability, node_count - 1)
origin_pop = pop
temp_pop = vstack((copy_pop, origin_pop))
func_obj = MinMax2(temp_pop, node_dirt)
pop = dominanceMain(temp_pop, func_obj)
print("第 %d 次迭代" % i)
# estimate(pop, func_obj)
pop_node = array(list(set([tuple(sorted(t))
for t in pop]))) # 个体按数值大小排序, 去重
return pop_node
for i in range(N):
r = random()
n = randint(0, V // 2) # 在个体的前半段随机选取一个点进行变异
m = randint(V // 2, V - 1) # 在个体的后半段随机选取一个点进行变异
if r <= belta:
if population[i][n] + 1 <= max_num:
population[i][n] = population[i][n] + 1 # 整数编码,随机+1或者减1
if population[i][m] - 1 >= 0:
population[i][m] = population[i][m] - 1
for i in range(N):
m = len(set(population[i]))
# print(m)
if m < V:
# print(population[i],old_population[i])
population[i] = old_population[i]
# 以下是测试用例
if __name__ == "__main__":
from numpy import random
random.seed(0)
xN = 5
yN = 3
belta = 1
p = population(4, 7)
print(p)
mutation(p, belta, 29)
print(p)
V = population.shape[1] # 列
for i in range(N - 1):
r = random()
if r < alfa:
i_p = V // 2
for n in range(i_p, V): # 选择是在个体一半处进行交叉,交换两个个体的后半段
mid = population[i][n]
population[i][n] = population[i + 1][n]
population[i + 1][n] = mid
# for i in range(N-1):
# population[i]=np.sort(population[i]) # 个体基因大小的排序
for i in range(N):
m = len(set(population[i]))
if m < V:
population[i] = old_population[i]
# 以下是测试用例
if __name__ == "__main__":
from numpy import random
random.seed(0)
xN = 5
yN = 3
alfa = 0.8
p = population(5, 6, 13)
print(p)
crossover(p, alfa)
print(p)
N = ceng_population.shape[0]
n = len(array(list(set([tuple(t) for t in ceng_population]))))
print("第 ", i, "层的重复率:", n / N, "--该层个体数量:", N, "--不重复个体数量:", n)
print(len(indicate))
# 返回新种群
return population[indicate]
if __name__ == "__main__":
#np.random.seed(0)
#random.seed(0) # 显示此行会固定初始化的种群
#from function.funUserDefine import *
#population=np.random.rand(10, 3)
#print(population)
#functionObject=ZDT1(population)
#print(functionObject)
#print(dominanceMain(population, functionObject))
print("#################################")
p = population(20, 3, 94)
#print(p)
from function.funUserDefine import *
f = MinMax2(p)
#print(f.objFun_1())
#print(f.objFun_2())
#d = dominanceMain(p, f)
d = dominanceMain(p, f)
print(d)
monitored = monitored + float(pDirt[str(i)][3])
# 2 单以管长来计算
for i in monitorednode:
monitored = monitored + float(pDirt[str(i)][2])
'''
monitored = 0
for i in monitored_node:
monitored = monitored + float(node_dirt[str(i)][2])
#monitored = len(monitorednode)*(1/66383) # 1.平均概率
#monitored = len(monitorednode)/len(pDirt)
unmonitored = 1 - monitored
unmonitored = float('%.3f' % unmonitored)
unmonitored_result.append(unmonitored)
return unmonitored_result
# 测试函数 如下
if __name__ == "__main__":
from population_init import population
import numpy as np
import json
jsonFile = "F:\\AWorkSpace\\data\\3628node.json"
with open(jsonFile, "r") as f:
nodeJson = json.load(f)
nodeDirt2 = json.loads(nodeJson)
p = population(500, 100, 3628)
m3 = MinMax2(p, nodeDirt2)
print(m3.objFun_1())
print(m3.objFun_2())