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============================
population.initChrom() # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
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)]
# 进行进化操作，分别对各个种群染色体矩阵进行重组和变异
for i in range(population.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) #重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
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
NVAR = self.problem.Dim # 得到决策变量的个数
self.obj_trace = (np.zeros(
(self.MAXGEN, 2)) * np.nan) # 定义目标函数值记录器，初始值为nan
self.var_trace = (np.zeros(
(self.MAXGEN, NVAR)) * np.nan) # 定义变量记录器，记录决策变量值，初始值为nan
self.forgetCount = 0 # “遗忘策略”计数器，用于记录连续出现最优个体不是可行个体的代数
#===========================准备进化============================
self.timeSlot = time.time() # 开始计时
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）
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
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============================
self.currentGen = 0
while self.terminated(population) == False:
bestIdx = np.argmax(population.FitnV,
axis=0) # 得到当代的最优个体的索引, 设置axis=0可使得返回一个向量
studPop = population[np.tile(
bestIdx, (NIND // 2))] # 复制最优个体NIND//2份，组成一个“种马种群”
restPop = population[np.where(
np.array(range(NIND)) != bestIdx)[0]] # 得到除去精英个体外其它个体组成的种群
# 选择个体，以便后面与种马种群进行交配
tempPop = restPop[ea.selecting(self.selFunc, restPop.FitnV,
(NIND - studPop.sizes))]
# 将种马种群与选择出来的个体进行合并
population = studPop + tempPop
# 进行进化操作
population.Chrom = ea.recombin(self.recFunc, population.Chrom,
self.pc) # 重组
population.Chrom = ea.mutate(self.mutFunc, population.Encoding,
population.Chrom, population.Field,
self.pm) # 变异
# 求进化后个体的目标函数值
population.Phen = population.decoding() # 染色体解码
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV)
self.evalsNum += population.sizes # 更新评价次数
population.FitnV = ea.scaling(self.problem.maxormins *
population.ObjV,
population.CV) # 计算适应度
# 处理进化记录器
delIdx = np.where(np.isnan(self.obj_trace))[0]
self.obj_trace = np.delete(self.obj_trace, delIdx, 0)
self.var_trace = np.delete(self.var_trace, delIdx, 0)
if self.obj_trace.shape[0] == 0:
raise RuntimeError(
'error: No feasible solution. (有效进化代数为0，没找到可行解。)')
self.passTime += time.time() - self.timeSlot # 更新用时记录
# 绘图
if self.drawing != 0:
ea.trcplot(self.obj_trace, [['种群个体平均目标函数值', '种群最优个体目标函数值']])
# 返回最后一代种群、进化记录器、变量记录器以及执行时间
return [population, self.obj_trace, self.var_trace]
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============================
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）
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
bestIndi = population[np.argmax(population.FitnV, 0)] # 得到当代的最优个体
# 选择
offspring = population[ea.selecting(self.selFunc, population.FitnV, NIND - 1)]
# 进行进化操作，分别对各种编码的染色体进行重组和变异
for i in range(population.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i], offspring.Chroms[i], self.pcs[i]) # 重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i], offspring.Encodings[i], offspring.Chroms[i], offspring.Fields[i], self.pms[i]) # 变异
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
self.problem.aimFunc(offspring) # 计算目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
population = bestIndi + offspring # 更新种群
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
def run(self):
#==========================初始化配置===========================
population = self.population
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
uniformPoint, NIND = ea.crtup(self.problem.M, population.sizes) # 生成在单位目标维度上均匀分布的参考点集
refPoint = uniformPoint.copy() # 初始化参考点为uniformPoint
if population.Chrom is None or population.sizes != NIND:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
self.problem.aimFunc(population) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
#===========================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 = ea.recombin(self.recFunc, offspring.Chrom, self.pc) # 重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding, offspring.Chrom, offspring.Field, self.pm) # 变异
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, refPoint)
# 修改refPoint
if (self.currentGen) % np.ceil(self.fr * self.MAXGEN) == 0:
refPoint = uniformPoint * (np.max(population.ObjV, 0) - np.min(population.ObjV, 0))
self.Gamma = None # 重置Gamma为None
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() # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
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:
# 进行差分进化操作
r0 = ea.selecting(self.selFunc, population.FitnV, NIND) # 得到基向量索引
offspring = population.copy() # 存储子代种群
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field, r0,
self.F, 1) # 差分变异
tempPop = population + offspring # 当代种群个体与变异个体进行合并（为的是后面用于重组）
offspring.Chrom = ea.recombin(self.recFunc, tempPop.Chrom, self.pc,
True) # 重组
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
self.problem.aimFunc(offspring)
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============================
population.initChrom(NIND) # 初始化种群染色体矩阵（内含染色体解码，详见PsyPopulation类的源码）
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
bestIdx = np.argmax(population.FitnV, axis = 0) # 得到当代的最优个体的索引, 设置axis=0可使得返回一个向量
studPop = population[np.tile(bestIdx, (NIND//2))] # 复制最优个体NIND//2份，组成一个“种马种群”
restPop = population[np.where(np.array(range(NIND)) != bestIdx)[0]] # 得到除去精英个体外其它个体组成的种群
# 选择个体，以便后面与种马种群进行交配
tempPop = restPop[ea.selecting(self.selFunc, restPop.FitnV, (NIND - studPop.sizes))]
# 将种马种群与选择出来的个体进行合并
population = studPop + tempPop
# 进行进化操作，分别对各种编码的染色体进行重组和变异
for i in range(population.ChromNum):
population.Chroms[i] = ea.recombin(self.recFuncs[i], population.Chroms[i], self.pcs[i]) # 重组
population.Chroms[i] = ea.mutate(self.mutFuncs[i], population.Encodings[i], population.Chroms[i], population.Fields[i], self.pms[i]) # 变异
# 求进化后个体的目标函数值
population.Phen = population.decoding() # 染色体解码
self.problem.aimFunc(population)
self.evalsNum += population.sizes # 更新评价次数
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
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）
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
# 选择
population = population[ea.selecting(self.selFunc,
population.FitnV, NIND)]
# 进行进化操作
population.Chrom = ea.recombin(self.recFunc, population.Chrom,
self.pc) # 重组
population.Chrom = ea.mutate(self.mutFunc, population.Encoding,
population.Chrom, population.Field,
self.pm) # 变异
# 求进化后个体的目标函数值
population.Phen = population.decoding() # 染色体解码
self.problem.aimFunc(population) # 计算目标函数值
self.evalsNum += population.sizes # 更新评价次数
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
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）
self.problem.aimFunc(population) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
while self.terminated(population) == False:
# 选择基个体
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 对选出的个体进行进化操作
offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom,
self.pc) #重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm) # 变异
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
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
# 进行差分进化操作
r0 = ea.selecting('ecs', population.FitnV, NIND) # 得到基向量索引，采用ecs复制精英个体索引
experimentPop = population.copy() # 存储试验个体
experimentPop.Chrom = ea.mutate(self.mutFunc, experimentPop.Encoding, experimentPop.Chrom, experimentPop.Field, r0, self.F, 1) # 差分变异
tempPop = population + experimentPop # 当代种群个体与变异个体进行合并（为的是后面用于重组）
experimentPop.Chrom = ea.recombin(self.recFunc, tempPop.Chrom, self.pc, True) # 重组
# 求进化后个体的目标函数值
experimentPop.Phen = experimentPop.decoding() # 染色体解码
self.problem.aimFunc(experimentPop) # 计算目标函数值
self.evalsNum += experimentPop.sizes # 更新评价次数
tempPop = population + experimentPop # 临时合并，以调用otos进行一对一生存者选择
tempPop.FitnV = ea.scaling(self.problem.maxormins * tempPop.ObjV, tempPop.CV) # 计算适应度
population = tempPop[ea.selecting('otos', tempPop.FitnV, NIND)] # 采用One-to-One Survivor选择，产生新一代种群
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
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
uniformPoint, NIND = ea.crtup(self.problem.M, population.sizes) # 生成在单位目标维度上均匀分布的参考点集
refPoint = np.vstack([uniformPoint, np.random.rand(NIND, self.problem.M)]) # 初始化参考点（详见注释中的参考文献）
if population.Chrom is None or population.sizes != NIND:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
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
#===========================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 = ea.recombin(self.recFunc, offspring.Chrom, self.pc) # 重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding, offspring.Chrom, offspring.Field, self.pm) # 变异
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, refPoint)
# 修改refPoint
refPoint[NIND:, :] = self.renewRefPoint(population.ObjV, refPoint[NIND:, :])
if (self.currentGen) % np.ceil(self.fr * self.MAXGEN) == 0:
refPoint[:NIND, :] = uniformPoint * (np.max(population.ObjV, 0) - np.min(population.ObjV, 0))
# 后续处理，限制种群规模（因为此时种群规模有可能大于NIND）
[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 = population[ea.refselect(self.problem.maxormins * population.ObjV, levels, criLevel, NIND, uniformPoint)] # 根据参考点选择个体
return self.finishing(population) # 调用finishing完成后续工作并返回结果
def Evolution(self):
start_time = time.time()
Objv = self.get_Objv_i(self.chrom)
best_ind = np.argmax(Objv * self.maxormins)
for gen in range(self.MAXGEN):
self.log.logger.info('==> This is No.%d GEN <==' % (gen))
FitnV = ea.ranking(Objv * self.maxormins)
Selch = self.chrom[ea.selecting('rws', FitnV, self.Nind - 1), :]
Selch = ea.recombin('xovsp', Selch, self.xov_rate)
Selch = ea.mutate('mutswap', 'RI', Selch, self.FieldDR)
NewChrom = np.vstack((self.chrom[best_ind, :], Selch))
Objv = self.get_Objv_i(NewChrom)
best_ind = np.argmax(Objv * self.maxormins)
self.obj_trace[gen, 0] = np.sum(Objv) / self.Nind #记录当代种群的目标函数均值
self.obj_trace[gen, 1] = Objv[best_ind] #记录当代种群最有给他目标函数值
self.var_trace[gen, :] = NewChrom[best_ind, :] #记录当代种群最有个体的变量值
self.log.logger.info(
'GEN=%d,best_Objv=%.5f,best_chrom_i=%s\n' %
(gen, Objv[best_ind], str(
NewChrom[best_ind, :]))) #记录每一代的最大适应度值和个体
end_time = time.time()
self.time = end_time - start_time
def multimin(AIM_M, AIM_F, NIND, NVAR, Base, MAXGEN, SUBPOP, GGAP, selectStyle,
recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
BaseV = ga.crtbase(NVAR, Base)
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtbp(NIND, BaseV) # 创建简单离散种群
ObjV = aimfuc(Chrom) # 计算种群目标函数值
NDSet = np.zeros((0, Chrom.shape[1])) # 定义帕累托最优解集合(初始为空集)
NDSetObjV = np.zeros((0, ObjV.shape[1])) # 定义帕累托最优解的目标函数值记录器
start_time = time.time() # 开始计时
# 开始进化！！
for gen in range(MAXGEN):
# 求种群的非支配个体以及基于被支配数的适应度
[FitnV, frontIdx] = ga.ndominfast(maxormin * ObjV)
# 更新帕累托最优集以及种群非支配个体的适应度
[FitnV, NDSet, NDSetObjV,
repnum] = ga.upNDSet(Chrom, maxormin * ObjV, FitnV, NDSet,
maxormin * NDSetObjV, frontIdx)
# 进行遗传操作！！
SelCh = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择
SelCh = ga.recombin(recombinStyle, SelCh, recopt, SUBPOP) #交叉
SelCh = ga.mut(SelCh, BaseV, pm) # 变异
ObjVSel = aimfuc(SelCh) # 求育种个体的目标函数值
# 求种群的非支配个体以及基于被支配数的适应度
[FitnVSel, frontIdx] = ga.ndominfast(maxormin * ObjVSel)
[Chrom, ObjV] = ga.reins(Chrom, SelCh, SUBPOP, 1, 1, FitnV, FitnVSel,
ObjV, ObjVSel) #重插入
end_time = time.time() # 结束计时
# 返回进化记录器、变量记录器以及执行时间
return [ObjV, NDSet, NDSetObjV, end_time - start_time]
def run(self):
#==========================初始化配置===========================
population = self.population
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
uniformPoint, NIND = ea.crtup(self.problem.M, population.sizes) # 生成在单位目标维度上均匀分布的参考点集
if population.Chrom is None or population.sizes != NIND:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
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
#===========================开始进化============================
while self.terminated(population) == False:
# 进行差分进化操作
r0 = ea.selecting(self.selFunc, population.FitnV, NIND) # 得到基向量索引
offspring = population.copy() # 存储子代种群
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding, offspring.Chrom, offspring.Field, r0, self.F, 1) # 差分变异
tempPop = population + offspring # 当代种群个体与变异个体进行合并（为的是后面用于重组）
offspring.Chrom = ea.recombin(self.recFunc, tempPop.Chrom, self.pc, True) # 重组
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
self.problem.aimFunc(offspring) # 计算目标函数值
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, uniformPoint)
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
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
uniformPoint, NIND = ea.crtup(self.problem.M,
population.sizes) # 生成在单位目标维度上均匀分布的参考点集
population.initChrom(
NIND
) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
self.problem.aimFunc(population) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
#===========================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)]
# 进行进化操作，分别对各个种群染色体矩阵进行重组和变异
for i in range(population.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) #重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
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,
uniformPoint)
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===========================
problem = self.problem
population = self.population
NIND = population.sizes
MAXSIZE = self.MAXSIZE
if MAXSIZE is None: # 检查MAXSIZE，默认取2倍的种群规模
MAXSIZE = 2 * NIND
aimFuc = problem.aimFuc # 获取目标函数地址
#===========================准备进化============================
self.timeSlot = time.time() # 开始计时
if population.Chrom is None:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
population.ObjV, population.CV = aimFuc(population.Phen,
population.CV) # 计算种群的目标函数值
population.FitnV, NDSet = updateNDSet(population, problem.maxormins,
MAXSIZE) # 计算适应度和得到全局非支配种群
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
self.currentGen = 0
while self.terminated(NDSet, population) == False:
uniChrom = np.unique(NDSet.Chrom, axis=0)
repRate = 1 - uniChrom.shape[0] / NDSet.sizes # 计算NDSet中的重复率
# 选择基个体去进化形成子代
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 对育种种群进行进化操作
offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom,
self.pc) #重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm) # 变异
if population.Encoding != 'B' and population.Encoding != 'G' and repRate > 0.1:
offspring.Chrom = ea.mutate('mutgau', offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm, False,
3) # 高斯变异，对标准差放大3倍。
offspring.Phen = offspring.decoding() # 染色体解码
offspring.ObjV, offspring.CV = aimFuc(offspring.Phen,
offspring.CV) # 求进化后个体的目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
# 父代种群和育种种群合并
population = population + offspring
population.FitnV, NDSet = updateNDSet(population,
problem.maxormins, MAXSIZE,
NDSet) # 计算合并种群的适应度及更新NDSet
# 保留个体到下一代
population = population[ea.selecting('dup', population.FitnV,
NIND)] # 选择，保留NIND个个体
NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解
self.passTime += time.time() - self.timeSlot # 更新用时记录
#=========================绘图及输出结果=========================
if self.drawing != 0:
ea.moeaplot(NDSet.ObjV, True)
# 返回帕累托最优集以及执行时间
return NDSet
def run(self):
#==========================初始化配置===========================
population = self.population
NIND = population.sizes
NVAR = self.problem.Dim # 得到决策变量的个数
self.obj_trace = (np.zeros(
(self.MAXGEN, 2)) * np.nan) # 定义目标函数值记录器，初始值为nan
self.var_trace = (np.zeros(
(self.MAXGEN, NVAR)) * np.nan) # 定义变量记录器，记录决策变量值，初始值为nan
self.forgetCount = 0 # “遗忘策略”计数器，用于记录连续出现最优个体不是可行个体的代数
#===========================准备进化============================
self.timeSlot = time.time() # 开始计时
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）
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
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============================
self.currentGen = 0
while self.terminated(population) == False:
# 选择
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 进行进化操作
offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom,
self.pc) # 重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm) # 变异
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
offspring.ObjV, offspring.CV = self.problem.aimFuc(
offspring.Phen, offspring.CV)
self.evalsNum += offspring.sizes # 更新评价次数
population = population + offspring # 父子合并
population.FitnV = ea.scaling(self.problem.maxormins *
population.ObjV,
population.CV) # 计算适应度
# 得到新一代种群
population = population[ea.selecting(self.selFunc,
population.FitnV, NIND)]
# 处理进化记录器
delIdx = np.where(np.isnan(self.obj_trace))[0]
self.obj_trace = np.delete(self.obj_trace, delIdx, 0)
self.var_trace = np.delete(self.var_trace, delIdx, 0)
if self.obj_trace.shape[0] == 0:
raise RuntimeError(
'error: No feasible solution. (有效进化代数为0，没找到可行解。)')
self.passTime += time.time() - self.timeSlot # 更新用时记录
# 绘图
if self.drawing != 0:
ea.trcplot(self.obj_trace, [['种群个体平均目标函数值', '种群最优个体目标函数值']])
# 返回最后一代种群、进化记录器、变量记录器以及执行时间
return [population, self.obj_trace, self.var_trace]
def run(self):
#==========================初始化配置===========================
problem = self.problem
population = self.population
NIND = population.sizes
MAXSIZE = self.MAXSIZE
if MAXSIZE is None: # 检查MAXSIZE，默认取2倍的种群规模
MAXSIZE = 2 * NIND
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
self.problem.aimFunc(population) # 计算种群的目标函数值
NDSet = updateNDSet(population, problem.maxormins,
MAXSIZE) # 计算适应度和得到全局非支配种群
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
while self.terminated(population) == False:
# 选择个体去进化形成子代
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 进行进化操作，分别对各个种群染色体矩阵进行重组和变异
for i in range(population.ChromNum):
uniChrom = np.unique(NDSet.Chroms[i], axis=0)
repRate = 1 - uniChrom.shape[0] / NDSet.sizes # 计算NDSet中的重复率
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) #重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
if population.Encodings[i] == 'RI' and repRate > 0.1:
offspring.Chroms[i] = ea.mutate('mutgau',
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i], False,
3) # 高斯变异，对标准差放大3倍。
offspring.Phen = offspring.decoding() # 染色体解码
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
# 父代种群和育种种群合并
population = population + offspring
NDSet = updateNDSet(population, problem.maxormins, MAXSIZE,
NDSet) # 计算合并种群的适应度及更新NDSet
# 保留个体到下一代
population = population[ea.selecting('dup', population.FitnV,
NIND)] # 选择，保留NIND个个体
NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解
self.passTime += time.time() - self.timeSlot # 更新用时记录
#=========================绘图及输出结果=========================
if self.drawing != 0:
ea.moeaplot(NDSet.ObjV, 'Pareto Front', True)
# 返回帕累托最优集
return NDSet
def run(self):
#==========================初始化配置===========================
population = self.population
NIND = population.sizes
NVAR = self.problem.Dim # 得到决策变量的个数
self.obj_trace = (np.zeros(
(self.MAXGEN, 2)) * np.nan) # 定义目标函数值记录器，初始值为nan
self.var_trace = (np.zeros(
(self.MAXGEN, NVAR)) * np.nan) # 定义变量记录器，记录决策变量值，初始值为nan
self.forgetCount = 0 # “遗忘策略”计数器，用于记录连续出现最优个体不是可行个体的代数
#===========================准备进化============================
self.timeSlot = time.time() # 开始计时
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）
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
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============================
self.currentGen = 0
while self.terminated(population) == False:
population.shuffle() # 打乱个体顺序
# 进行差分进化操作
mutPop = population.copy()
mutPop.Chrom = ea.mutate(self.mutFunc, mutPop.Encoding,
mutPop.Chrom, mutPop.Field, self.F,
1) # 差分变异
tempPop = population + mutPop # 当代种群个体与变异个体进行合并（为的是后面用于重组）
experimentPop = population.copy() # 试验种群
experimentPop.Chrom = ea.recombin(self.recFunc, tempPop.Chrom,
self.pc, True) # 重组
# 求进化后个体的目标函数值
experimentPop.Phen = experimentPop.decoding() # 染色体解码
experimentPop.ObjV, experimentPop.CV = self.problem.aimFuc(
experimentPop.Phen, experimentPop.CV)
self.evalsNum += experimentPop.sizes # 更新评价次数
tempPop = population + experimentPop # 临时合并，以调用otos进行一对一生存者选择
tempPop.FitnV = ea.scaling(self.problem.maxormins * tempPop.ObjV,
tempPop.CV) # 计算适应度
population = tempPop[ea.selecting(
'otos', tempPop.FitnV,
NIND)] # 采用One-to-One Survivor选择，产生新一代种群
# 处理进化记录器
delIdx = np.where(np.isnan(self.obj_trace))[0]
self.obj_trace = np.delete(self.obj_trace, delIdx, 0)
self.var_trace = np.delete(self.var_trace, delIdx, 0)
if self.obj_trace.shape[0] == 0:
raise RuntimeError(
'error: No feasible solution. (有效进化代数为0，没找到可行解。)')
self.passTime += time.time() - self.timeSlot # 更新用时记录
# 绘图
if self.drawing != 0:
ea.trcplot(self.obj_trace, [['种群个体平均目标函数值', '种群最优个体目标函数值']])
# 返回最后一代种群、进化记录器、变量记录器以及执行时间
return [population, self.obj_trace, self.var_trace]
def Evolution(self): #
start_time = time.time()
# 初始化种群
self.get_FieldDR()
Init_chrom = self.get_init_chrom()
# 开始进化
self.log.logger.info('==> This is Init GEN <==')
Init_Objv = self.get_Objv_i(Init_chrom)
best_ind = np.argmax(Init_Objv * self.maxormins) #记录最优个体的索引值
self.chrom_all = Init_chrom
self.Objv_all = Init_Objv
for gen in range(self.MAXGEN):
self.log.logger.info('==> This is No.%d GEN <==' % (gen))
if gen == 0: #第一代和后面有所不同
chrom = Init_chrom
Objv = Init_Objv
else:
chrom = NewChrom
Objv = NewObjv
FitnV = ea.ranking(Objv * self.maxormins)
Selch = chrom[ea.selecting('rws', FitnV, self.Nind -
1), :] #轮盘赌选择 Nind-1 代，与上一代的最优个体再进行拼接
Selch = ea.recombin('xovsp', Selch, self.xov_rate) #重组，即交叉
Selch = ea.mutate('mutswap', 'RI', Selch, self.FieldDR) #变异
Objv_Selch = self.get_Objv_i(Selch)
NewChrom = np.vstack(
(chrom[best_ind, :], Selch)) #将上一代的最优个体与现在的种群拼接
NewObjv = np.vstack((Objv[best_ind, :], Objv_Selch))
best_ind = np.argmax(NewObjv * self.maxormins)
self.chrom_all = np.vstack((self.chrom_all, NewChrom))
self.Objv_all = np.vstack((self.Objv_all, NewObjv))
self.obj_trace[gen,
0] = np.sum(NewObjv) / self.Nind # 记录当代种群的目标函数均值
self.obj_trace[gen, 1] = NewObjv[best_ind] # 记录当代种群最有给他目标函数值
self.var_trace[gen, :] = NewChrom[best_ind, :] # 记录当代种群最优个体的变量值
self.log.logger.info(
'GEN=%d,best_Objv=%.5f,best_chrom_i=%s\n' %
(gen, NewObjv[best_ind], str(
NewChrom[best_ind, :]))) # 记录每一代的最大适应度值和个体
self.Save_chroms(self.chrom_all)
self.Save_objvs(self.Objv_all)
end_time = time.time()
self.time = end_time - start_time
self.log.logger.info('The time of Evoluation is %.5f s. ' % self.time)
def run(self):
#==========================初始化配置===========================
self.ax = None # 存储上一桢动画
population = self.population
NIND = population.sizes
#===========================准备进化============================
self.timeSlot = time.time() # 开始计时
if population.Chrom is None:
population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
#传入的参数是Phen，也就是表现型矩阵值和约束矩阵(可行性矩阵)
self.evalsNum = population.sizes # 记录评价次数
population.FitnV = self.calFitnV(population, NIND) # 计算适应度
#===========================开始进化============================
self.currentGen = 0
while self.terminated(population) == False:
# 选择基个体
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 对基个体进行进化操作
offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom,
self.pc) #重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm) # 变异
offspring.Phen = offspring.decoding() # 解码
offspring.ObjV, offspring.CV = self.problem.aimFuc(
offspring.Phen, offspring.CV) # 求进化后个体的目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
# 合并
population = population + offspring
# 计算合并种群的适应度
population.FitnV = self.calFitnV(population, NIND)
# 选择个体保留到下一次进化
population = population[ea.selecting(
'dup', population.FitnV,
NIND)] # 调用低级选择算子dup进行基于适应度排序的选择，保留NIND个个体
# 得到非支配种群，新的父代产生
[levels,
criLevel] = self.ndSort(self.problem.maxormins * population.ObjV,
NIND, 1, population.CV) # 非支配分层
#划分一层，提取出rank0
NDSet = population[np.where(levels == 1)[0]] # 只保留种群中的非支配个体，形成一个非支配种群
NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解
self.passTime += time.time() - self.timeSlot # 更新用时记录
#=========================绘图及输出结果=========================
if self.drawing != 0:
ea.moeaplot(NDSet.ObjV, True)
# 返回帕累托最优集
return NDSet
def mintemp1(AIM_M, AIM_F, PUN_M, PUN_F, ranges, borders, MAXGEN, NIND, SUBPOP,
GGAP, selectStyle, recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
punishing = getattr(PUN_M, PUN_F) # 获得罚函数
FieldDR = ga.crtfld(ranges, borders) # 初始化区域描述器
NVAR = ranges.shape[1] # 得到控制变量的个数
# 定义进化记录器，初始值为nan
pop_trace = (np.zeros((MAXGEN, 3)) * np.nan).astype('int64')
# 定义变量记录器，记录控制变量值，初始值为nan
var_trace = (np.zeros((MAXGEN, NVAR)) * np.nan).astype('int64')
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtip(NIND, FieldDR) # 根据区域描述器FieldDR生成整数型初始种群
LegV = np.ones((NIND, 1)) # 生成可行性列向量，元素为1表示对应个体是可行解，0表示非可行解
[ObjV, LegV] = aimfuc(Chrom, LegV) # 计算种群目标函数值，同时更新LegV
start_time = time.time() # 开始计时
# 开始进化！！
for gen in range(MAXGEN):
FitnV = ga.ranking(maxormin * ObjV, LegV) # 计算种群适应度
FitnV = punishing(LegV, FitnV) # 调用罚函数
# 记录进化过程
bestIdx = np.argmax(FitnV)
if LegV[bestIdx] != 0:
feasible = np.where(LegV != 0)[0] # 排除非可行解
# 记录当代种群的适应度均值
pop_trace[gen,
1] = np.sum(FitnV[feasible]) / FitnV[feasible].shape[0]
# 记录当代种群最优个体的目标函数值
pop_trace[gen, 0] = ObjV[bestIdx]
# 记录当代种群的最优个体的适应度值
pop_trace[gen, 2] = FitnV[bestIdx]
# 记录当代种群最优个体的变量值
var_trace[gen, :] = Chrom[bestIdx, :]
# 进行遗传操作！！
SelCh = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择
SelCh = ga.recombin(recombinStyle, SelCh, recopt, SUBPOP) #交叉
SelCh = ga.mutint(SelCh, FieldDR, pm) # 实值变异
LegVSel = np.ones((SelCh.shape[0], 1)) # 创建育种个体的可行性列向量
[ObjVSel, LegVSel] = aimfuc(SelCh, LegVSel) # 求育种个体的目标函数值
FitnVSel = punishing(LegVSel, FitnV) # 调用罚函数
[Chrom, ObjV,
LegV] = ga.reins(Chrom, SelCh, SUBPOP, 1, 1, FitnV, FitnVSel, ObjV,
ObjVSel, LegV, LegVSel) #重插入
end_time = time.time() # 结束计时
# 后处理进化记录器
delIdx = np.where(np.isnan(pop_trace))[0]
pop_trace = np.delete(pop_trace, delIdx, 0)
var_trace = np.delete(var_trace, delIdx, 0)
# 返回进化记录器、变量记录器以及执行时间
return [pop_trace, var_trace, end_time - start_time]
def nsga2(AIM_M, AIM_F, NIND, ranges, borders, precisions, MAXGEN, SUBPOP,
GGAP, selectStyle, recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
GGAP = 0.5 * GGAP # 为了避免父子两代合并后种群数量爆炸，要让代沟不超过0.5
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
FieldDR = ga.crtfld(ranges, borders, precisions)
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtrp(NIND, FieldDR) # 创建简单离散种群
ObjV = aimfuc(Chrom) # 计算种群目标函数值
NDSet = np.zeros((0, ObjV.shape[1])) # 定义帕累托最优解集合(初始为空集)
start_time = time.time() # 开始计时
[FitnV, levels, maxLevel] = ga.ndomindeb(maxormin * ObjV, 1) # deb非支配分级
frontIdx = np.where(levels == 1)[0] # 处在第一级的个体即为种群的非支配个体
# 更新帕累托最优集以及种群非支配个体的适应度
[FitnV, NDSet, repnum] = ga.upNDSet(FitnV, maxormin * ObjV,
maxormin * NDSet, frontIdx)
# 开始进化！！
for gen in range(MAXGEN):
# if NDSet.shape[0] > 2 * ObjV.shape[0]:
# break
# 进行遗传操作！！
SelCh = ga.recombin(recombinStyle, Chrom, recopt, SUBPOP) #交叉
SelCh = ga.mutbga(SelCh, FieldDR, pm) # 变异
if repnum > Chrom.shape[0] * 0.05: # 当最优个体重复率高达5%时，进行一次高斯变异
SelCh = ga.mutgau(SelCh, FieldDR, pm) # 高斯变异
# 父子合并
Chrom = np.vstack([Chrom, SelCh])
ObjV = aimfuc(Chrom) # 求目标函数值
[FitnV, levels, maxLevel] = ga.ndomindeb(maxormin * ObjV,
1) # deb非支配分级
frontIdx = np.where(levels == 1)[0] # 处在第一级的个体即为种群的非支配个体
# 更新帕累托最优集以及种群非支配个体的适应度
[FitnV, NDSet, repnum] = ga.upNDSet(FitnV, maxormin * ObjV,
maxormin * NDSet, frontIdx)
# 计算每个目标下个体的聚集距离(不需要严格计算欧氏距离，计算绝对值即可)
for i in range(ObjV.shape[1]):
idx = np.argsort(ObjV[:, i], 0)
dis = np.abs(np.diff(ObjV[idx, i].T, 1).T) / (
np.max(ObjV[idx, i]) - np.min(ObjV[idx, i]) + 1) # 差分计算距离
dis = np.hstack([dis, dis[-1]])
FitnV[idx, 0] += dis # 根据聚集距离修改适应度，以增加种群的多样性
Chrom = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择出下一代
end_time = time.time() # 结束计时
# 返回帕累托最优集以及执行时间
return [ObjV, NDSet, end_time - start_time]
def run(self):
#==========================初始化配置===========================
population = self.population
self.timeSlot = time.time() # 开始计时
uniformPoint, NIND = ea.crtup(self.problem.M,
population.sizes) # 生成在单位目标维度上均匀分布的参考点集
if population.Chrom is None or population.sizes != NIND:
population.initChrom(
NIND
) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
self.currentGen = 0
while self.terminated(population) == False:
population.shuffle() # 打乱个体顺序
# 进行差分进化操作
mutPop = population.copy()
mutPop.Chrom = ea.mutate(self.mutFunc, mutPop.Encoding,
mutPop.Chrom, mutPop.Field, self.F,
1) # 差分变异
tempPop = population + mutPop # 当代种群个体与变异个体进行合并（为的是后面用于重组）
experimentPop = population.copy() # 试验种群
experimentPop.Chrom = ea.recombin(self.recFunc, tempPop.Chrom,
self.pc, True) # 重组
# 求进化后个体的目标函数值
experimentPop.Phen = experimentPop.decoding() # 染色体解码
experimentPop.ObjV, experimentPop.CV = self.problem.aimFuc(
experimentPop.Phen, experimentPop.CV)
self.evalsNum += experimentPop.sizes # 更新评价次数
# 合并
population = population + experimentPop
population.FitnV = self.calFitnV(population, NIND,
uniformPoint) # 计算合并种群的适应度
population = population[ea.selecting('dup', population.FitnV,
NIND)] # 选择操作，保留NIND个个体
# 得到非支配种群
[levels,
criLevel] = self.ndSort(self.problem.maxormins * population.ObjV,
NIND, 1, population.CV) # 非支配分层
NDSet = population[np.where(levels == 1)[0]] # 只保留种群中的非支配个体，形成一个非支配种群
NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解
self.passTime += time.time() - self.timeSlot # 更新用时记录
# 绘图
if self.drawing != 0:
ea.moeaplot(NDSet.ObjV, True)
# 返回帕累托最优集
return NDSet
def run(self):
#==========================初始化配置===========================
population = self.population
self.timeSlot = time.time() # 开始计时
uniformPoint, NIND = ea.crtup(self.problem.M,
population.sizes) # 生成在单位目标维度上均匀分布的参考点集
if population.Chrom is None or population.sizes != NIND:
population.initChrom(
NIND
) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置
population.ObjV, population.CV = self.problem.aimFuc(
population.Phen, population.CV) # 计算种群的目标函数值
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
self.currentGen = 0
while self.terminated(population) == False:
# 选择个体参与进化
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 对基个体进行进化操作
offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom,
self.pc) #重组
offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding,
offspring.Chrom, offspring.Field,
self.pm) # 变异
offspring.Phen = offspring.decoding() # 解码
offspring.ObjV, offspring.CV = self.problem.aimFuc(
offspring.Phen, offspring.CV) # 求进化后个体的目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
# 合并
population = population + offspring
population.FitnV = self.calFitnV(population, NIND,
uniformPoint) # 计算合并种群的适应度
population = population[ea.selecting('dup', population.FitnV,
NIND)] # 选择操作，保留NIND个个体
# 得到非支配种群
[levels,
criLevel] = self.ndSort(self.problem.maxormins * population.ObjV,
NIND, 1, population.CV) # 非支配分层
NDSet = population[np.where(levels == 1)[0]] # 只保留种群中的非支配个体，形成一个非支配种群
NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解
self.passTime += time.time() - self.timeSlot # 更新用时记录
# 绘图
if self.drawing != 0:
ea.moeaplot(NDSet.ObjV, True)
# 返回帕累托最优集
return NDSet
def run(self):
#==========================初始化配置===========================
population = self.population
NIND = population.sizes
if NIND < 2:
raise RuntimeError('error: Population'
' size is too small. (种群规模不能小于2。)')
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
population.initChrom(NIND) # 初始化种群染色体矩阵（内含染色体解码，详见PsyPopulation类的源码）
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
# 选择
chooseIdx = ea.selecting(self.selFunc, population.FitnV, 2)
offspring = population[chooseIdx]
# 进行进化操作，分别对各种编码的染色体进行重组和变异
for i in range(population.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) # 重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
self.problem.aimFunc(offspring) # 计算目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
tempPop = population + offspring # 父子合并
tempPop.FitnV = ea.scaling(self.problem.maxormins * tempPop.ObjV,
tempPop.CV) # 计算适应度
# 得到新一代种群
tempPop = tempPop[ea.selecting('otos', tempPop.FitnV,
2)] # 采用One-to-One Survivor选择
population[chooseIdx] = tempPop
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
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============================
population.initChrom() # 初始化种群染色体矩阵（内含解码，详见Population类的源码）
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
globalNDSet = population[np.where(
levels == 1)[0]] # 创建全局存档，该全局存档贯穿进化始终，随着进化不断更新
globalNDSet = globalNDSet[np.where(np.all(globalNDSet.CV <= 0,
1))[0]] # 排除非可行解
#===========================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)]
# 进行进化操作，分别对各个种群染色体矩阵进行重组和变异
for i in range(population.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) #重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
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，同时更新全局存档
population, globalNDSet = self.reinsertion(population, offspring,
NIND, globalNDSet)
self.passTime += time.time() - self.timeSlot # 更新用时记录
#=========================绘图及输出结果=========================
if self.drawing != 0:
ea.moeaplot(globalNDSet.ObjV, 'Pareto Front', True)
# 返回帕累托最优集
return globalNDSet
def run(self):
#==========================初始化配置===========================
population = self.population
NIND = population.sizes
GGAP_NUM = int(np.ceil(NIND * self.GGAP)) # 计算每一代替换个体的个数
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
population.initChrom(NIND) # 初始化种群染色体矩阵（内含染色体解码，详见PsyPopulation类的源码）
self.problem.aimFunc(population) # 计算种群的目标函数值
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============================
while self.terminated(population) == False:
# 选择
offspring = population[ea.selecting(self.selFunc, population.FitnV,
NIND)]
# 进行进化操作，分别对各种编码的染色体进行重组和变异
for i in range(offspring.ChromNum):
offspring.Chroms[i] = ea.recombin(self.recFuncs[i],
offspring.Chroms[i],
self.pcs[i]) # 重组
offspring.Chroms[i] = ea.mutate(self.mutFuncs[i],
offspring.Encodings[i],
offspring.Chroms[i],
offspring.Fields[i],
self.pms[i]) # 变异
# 求进化后个体的目标函数值
offspring.Phen = offspring.decoding() # 染色体解码
self.problem.aimFunc(offspring) # 计算目标函数值
self.evalsNum += offspring.sizes # 更新评价次数
offspring.FitnV = ea.scaling(self.problem.maxormins *
offspring.ObjV, offspring.CV) # 计算适应度
# 根据代沟把子代重插入到父代生成新一代种群
population = self.reinsertion(population, offspring, GGAP_NUM)
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
def i_awGA(AIM_M, AIM_F, NIND, ranges, borders, precisions, MAXGEN, SUBPOP,
GGAP, selectStyle, recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
FieldDR = ga.crtfld(ranges, borders, precisions)
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtrp(NIND, FieldDR) # 创建简单离散种群
ObjV = aimfuc(Chrom) # 计算种群目标函数值
NDSet = np.zeros((0, ObjV.shape[1])) # 定义帕累托最优解集合(初始为空集)
start_time = time.time() # 开始计时
# 开始进化！！
for gen in range(MAXGEN):
if NDSet.shape[0] > 2 * ObjV.shape[0]:
break
[CombinObjV, weight] = ga.awGA(ObjV) # 适应性权重法求聚合目标函数值
FitnV = ga.ranking(maxormin * CombinObjV) # 根据加权单目标计算适应度
[FitnV, frontIdx] = ga.ndominfast(maxormin * ObjV,
FitnV) # 求种群的非支配个体，并更新适应度
# 更新帕累托最优集以及种群非支配个体的适应度
[FitnV, NDSet, repnum] = ga.upNDSet(FitnV, maxormin * ObjV,
maxormin * NDSet, frontIdx)
# 进行遗传操作！！
SelCh = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择
SelCh = ga.recombin(recombinStyle, SelCh, recopt, SUBPOP) #交叉
SelCh = ga.mutbga(SelCh, FieldDR, pm) # 变异
if repnum > Chrom.shape[0] * 0.1: # 进行一次高斯变异
SelCh = ga.mutgau(SelCh, FieldDR, pm) # 高斯变异
ObjVSel = aimfuc(SelCh) # 求育种个体的目标函数值
[CombinObjV, weight] = ga.awGA(maxormin * ObjVSel) # 适应性权重法求聚合目标函数值
FitnVSel = ga.ranking(maxormin * CombinObjV) # 根据聚合目标求育种个体适应度
[Chrom, ObjV] = ga.reins(Chrom, SelCh, SUBPOP, 1, 0.9, FitnV, FitnVSel,
ObjV, ObjVSel) #重插入
end_time = time.time() # 结束计时
# 返回帕累托最优集以及执行时间
return [ObjV, NDSet, end_time - start_time]
def q_sorted(AIM_M, AIM_F, NIND, ranges, borders, precisions, MAXGEN, SUBPOP,
GGAP, selectStyle, recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
FieldDR = ga.crtfld(ranges, borders, precisions)
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtrp(NIND, FieldDR) # 创建简单离散种群
ObjV = aimfuc(Chrom) # 计算种群目标函数值
NDSet = np.zeros((0, ObjV.shape[1])) # 定义帕累托最优解集合(初始为空集)
start_time = time.time() # 开始计时
ax = None
# 开始进化！！
for gen in range(MAXGEN):
# if NDSet.shape[0] > ObjV.shape[0]:
# break
# 求种群的非支配个体以及基于被支配数的适应度
[FitnV, frontIdx] = ga.ndominfast(maxormin * ObjV)
# 更新帕累托最优集以及种群非支配个体的适应度
[FitnV, NDSet, repnum] = ga.upNDSet(FitnV, maxormin * ObjV,
maxormin * NDSet, frontIdx)
# 进行遗传操作！！
SelCh = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择
SelCh = ga.recombin(recombinStyle, SelCh, recopt, SUBPOP) #交叉
SelCh = ga.mutbga(SelCh, FieldDR, pm) # 变异
if repnum > Chrom.shape[0] * 0.1: # 进行一次高斯变异
SelCh = ga.mutgau(SelCh, FieldDR, pm) # 高斯变异
ObjVSel = aimfuc(SelCh) # 求育种个体的目标函数值
# 求种群的非支配个体以及基于被支配数的适应度
[FitnVSel, frontIdx] = ga.ndominfast(maxormin * ObjVSel)
[Chrom, ObjV] = ga.reins(Chrom, SelCh, SUBPOP, 1, 0.9, FitnV, FitnVSel,
ObjV, ObjVSel) #重插入
ax = ga.frontplot(NDSet, False, ax, gen + 1)
end_time = time.time() # 结束计时
# 返回帕累托最优集以及执行时间
return [ObjV, NDSet, end_time - start_time]
def mintemp1(AIM_M, AIM_F, PUN_M, PUN_F, ranges, borders, MAXGEN, NIND, SUBPOP,
GGAP, selectStyle, recombinStyle, recopt, pm, maxormin):
"""==========================初始化配置==========================="""
# 获取目标函数和罚函数
aimfuc = getattr(AIM_M, AIM_F) # 获得目标函数
punishing = getattr(PUN_M, PUN_F) # 获得罚函数
FieldDR = ga.crtfld(ranges, borders) # 初始化区域描述器
NVAR = ranges.shape[1] # 得到控制变量的个数
# 定义进化记录器，初始值为nan
pop_trace = (np.zeros((MAXGEN, 3)) * np.nan).astype('int64')
# 定义变量记录器，记录控制变量值，初始值为nan
var_trace = (np.zeros((MAXGEN, NVAR)) * np.nan).astype('int64')
"""=========================开始遗传算法进化======================="""
Chrom = ga.crtip(NIND, FieldDR) # 根据区域描述器FieldDR生成整数型初始种群
ObjV = aimfuc(Chrom) # 计算种群目标函数值
start_time = time.time() # 开始计时
# 开始进化！！
for gen in range(MAXGEN):
FitnV = ga.ranking(maxormin * ObjV) # 计算种群适应度
FitnV = punishing(Chrom, FitnV) # 调用罚函数
# 记录当代种群最优个体的目标函数值
pop_trace[gen, 0] = ObjV[np.argmax(FitnV)]
# 记录当代种群的适应度均值
pop_trace[gen, 1] = np.sum(FitnV) / FitnV.shape[0]
# 记录当代种群的最优个体的适应度值
pop_trace[gen, 2] = np.max(FitnV)
# 记录当代种群最优个体的变量值
var_trace[gen, :] = Chrom[np.argmax(FitnV), :]
# 进行遗传操作！！
SelCh = ga.selecting(selectStyle, Chrom, FitnV, GGAP, SUBPOP) # 选择
SelCh = ga.recombin(recombinStyle, SelCh, recopt, SUBPOP) #交叉
SelCh = ga.mutint(SelCh, FieldDR, pm) # 实值变异
ObjVSel = aimfuc(SelCh) # 求育种个体的目标函数值
[Chrom,ObjV] = ga.reins(Chrom,SelCh,SUBPOP,2,1,maxormin*ObjV,\
maxormin*ObjVSel) #重插入
end_time = time.time() # 结束计时
# 返回进化记录器、变量记录器以及执行时间
return [pop_trace, var_trace, end_time - start_time]
