def run(self):
# ==========================初始化配置===========================
population = self.population
NIND = population.sizes
self.initialization() # 初始化算法模板的一些动态参数
# ===========================准备进化============================
if population.Chrom is None:
population.initChrom() # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
else:
population.Phen = population.decoding() # 染色体解码
self.problem.aimFunc(population) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
[levels,
criLevel] = self.ndSort(self.problem.maxormins * population.ObjV,
NIND, None, population.CV) # 对NIND个个体进行非支配分层
population.FitnV[:, 0] = 1 / levels # 直接根据levels来计算初代个体的适应度
# ===========================开始进化============================
while self.terminated(population) == False:
# 选择个体参与进化
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 对选出的个体进行进化操作
offspring.Chrom = self.recOper.do(offspring.Chrom) # 重组
offspring.Chrom = self.mutOper.do(offspring.Encoding,
offspring.Chrom,
offspring.Field) # 变异
#====================================设置对每条染色体，小于其最大值的参数设置为0======================================
algorithms.check_toZero_random(oldChrom=offspring.Chrom, pz=0.3)
#offspring.Chrom[offspring.Chrom < 1] = 0
offspring.Phen = offspring.decoding() # 解码
self.problem.aimFunc(offspring) # 求进化后个体的目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
# 重插入生成新一代种群
population = self.reinsertion(population, offspring, NIND)
return self.finishing(population) # 调用finishing完成后续工作并返回结果
def run(self):
# ==========================初始化配置===========================
population = self.population
NIND = population.sizes
self.initialization() # 初始化算法模板的一些动态参数
# ===========================准备进化============================
if population.Chrom is None:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含染色体解码，详见Population类的源码）
else:
population.Phen = population.decoding() # 染色体解码
# print(population.Chrom)
# print(population.Chrom.shape, population.ObjV.shape, population.CV.shape)
self.problem.aimFunc(population) # 计算种群的目标函数值
# print(population.Chrom.shape, population.ObjV.shape, population.CV.shape)
population.FitnV = ea.scaling(self.problem.maxormins * population.ObjV,
population.CV) # 计算适应度
self.evalsNum = population.sizes # 记录评价次数
# ========================================开始进化===============================================================
# 在这个判断条件中, terminated中会记录population的迭代记录
while self.terminated(population) == False:
# 进行迭代的时候，选择对每一个个体一次差分进化
u_population = [
population.copy(),
population.copy(),
population.copy()
] # 存储试验个体
indexes = [t for t in range(NIND)]
for i in range(NIND):
# 使用随机抽样来找出不同于当前处理的个体的另外五个不同个体索引，用来生成三个子代后代
indexes.remove(i)
r_list = random.sample(indexes, 5)
indexes.append(i)
# print(self.population.Field)
# print(self.population.Field.shape)
# print(self.population.Field[0])
# print(self.population.Field[1])
# 使用 rand/1/bin生成第一个差分算子
parameter_index = random.randint(0, 2) # 从参数池中随机选择一个参数
u_population[0].Chrom[i] = algorithms.mutde_1(
population.Chrom[r_list[0]], population.Chrom[r_list[1]],
population.Chrom[r_list[2]],
self.mut_parameters_pool[parameter_index])
u_population[0].Chrom[i] = algorithms.binary_recombination(
population.Chrom[i], u_population[0].Chrom[i],
self.bin_parameters_pool[parameter_index],
self.population.Field[1], self.population.Field[0])
# 使用 rand/2/bin生成第二个差分算子
u_population[1].Chrom[i] = algorithms.mutde_2(
population.Chrom[r_list[0]], population.Chrom[r_list[1]],
population.Chrom[r_list[2]], population.Chrom[r_list[3]],
population.Chrom[r_list[4]],
self.mut_parameters_pool[parameter_index])
u_population[1].Chrom[i] = algorithms.binary_recombination(
population.Chrom[i], u_population[1].Chrom[i],
self.bin_parameters_pool[parameter_index],
self.population.Field[1], self.population.Field[0])
# 使用 current-to-rand/1生成第三个差分算子
u_population[2].Chrom[i] = algorithms.mutde_current_to_rand(
population.Chrom[i], population.Chrom[r_list[0]],
population.Chrom[r_list[1]], population.Chrom[r_list[2]],
self.mut_parameters_pool[parameter_index],
self.population.Field[1], self.population.Field[0])
# 计算进化后个体的目标函数值
for u in u_population:
algorithms.check_toZero_random(oldChrom=u.Chrom, pz=0.1)
u.Phen = u.decoding() # 染色体解码
self.problem.aimFunc(u) # 计算目标函数值
self.evalsNum += u.sizes
tempPop = population + u_population[0] + u_population[
1] + u_population[2]
tempPop.FitnV = ea.scaling(self.problem.maxormins * tempPop.ObjV,
tempPop.CV)
population = tempPop[ea.selecting('otos', tempPop.FitnV, NIND)]
# for i in range(NIND):
# flag = 0
# if u_population[flag].ObjV[i] < u_population[1].ObjV[i]:
# flag = 1
# if u_population[flag].ObjV[i] < u_population[2].ObjV[i]:
# flag = 2
# if u_population[flag].ObjV[i] > population.ObjV[i]:
# population.Chrom[i] = u_population[flag].Chrom[i]
# population.ObjV[i] = u_population[flag].ObjV[i]
# population.CV[i] = u_population[flag].CV[i]
# population.Phen[i] = u_population[flag].Phen[i]
# population.FitnV[i] = u_population[flag].FitnV[i]
#
# population.FitnV = ea.scaling(self.problem.maxormins * population.ObjV, population.CV) # 计算适应度
return self.finishing(population) # 调用finishing完成后续工作并返回结果