def stat(self, population): # 分析记录，更新进化记录器，population为传入的种群对象
feasible = np.where(
np.all(population.CV <= 0,
1))[0] if population.CV is not None else np.array(
range(population.sizes)) # 找到可行解个体的下标
if len(feasible) > 0:
tempPop = population[feasible] # 获取可行解个体
self.pop_trace.append(tempPop) # 添加记录（只添加可行解个体到种群记录器中）
self.forgetCount = 0 # “遗忘策略”计数器清零
self.passTime += time.time() - self.timeSlot # 更新用时记录
if self.drawing == 2:
# 绘制目标空间动态图
self.ax = ea.moeaplot(tempPop.ObjV,
'objective values',
False,
self.ax,
self.currentGen,
gridFlag=True)
elif self.drawing == 3:
# 绘制决策空间动态图
self.ax = ea.varplot(tempPop.Phen,
'decision variables',
False,
self.ax,
self.currentGen,
gridFlag=False)
self.timeSlot = time.time() # 更新时间戳
else:
self.currentGen -= 1 # 忽略这一代
self.forgetCount += 1 # “遗忘策略”计数器加1
def stat(
self,
population): # 分析记录，更新进化记录器，population为传入的种群对象，NDSet为当代的种群中的非支配个体集
feasible = np.where(np.all(population.CV <= 0, 1))[0] # 找到可行解个体的下标
if len(feasible) > 0:
self.pop_trace.append(population) # 添加记录
self.forgetCount = 0 # “遗忘策略”计数器清零
self.passTime += time.time() - self.timeSlot # 更新用时记录
if self.drawing == 2:
# 绘制目标空间动态图
self.ax = ea.moeaplot(population.ObjV,
'objective values',
False,
self.ax,
self.currentGen,
gridFlag=True)
elif self.drawing == 3:
# 绘制决策空间动态图
self.ax = ea.varplot(population.Phen,
'decision variables',
False,
self.ax,
self.currentGen,
gridFlag=False)
self.timeSlot = time.time() # 更新时间戳
else:
self.currentGen -= 1 # 忽略这一代
self.forgetCount += 1 # “遗忘策略”计数器加1
def draw(self, pop, EndFlag=False):
"""
描述:
该函数用于在进化过程中进行绘图。该函数在stat()以及finishing函数里面被调用。
输入参数:
pop : class <Population> - 种群对象。
EndFlag : bool - 表示是否是最后一次调用该函数。
输出参数:
无输出参数。
"""
if not EndFlag:
self.passTime += time.time() - self.timeSlot # 更新用时记录，不计算画图的耗时
# 绘制动画
if self.drawing == 2:
# 绘制目标空间动态图
self.ax = ea.moeaplot(pop.ObjV,
'objective values',
False,
self.ax,
self.currentGen,
gridFlag=True)
elif self.drawing == 3:
# 绘制决策空间动态图
self.ax = ea.varplot(pop.Phen,
'decision variables',
False,
self.ax,
self.currentGen,
gridFlag=False)
self.timeSlot = time.time() # 更新时间戳
else:
# 绘制最终结果图
if self.drawing != 0:
if pop.ObjV.shape[1] == 2 or pop.ObjV.shape[1] == 3:
ea.moeaplot(pop.ObjV,
'Pareto Front',
saveFlag=True,
gridFlag=True)
else:
ea.moeaplot(pop.ObjV,
'Value Path',
saveFlag=True,
gridFlag=False)
def draw(self, pop, EndFlag=False):
"""
描述:
该函数用于在进化过程中进行绘图。该函数在stat()以及finishing函数里面被调用。
输入参数:
pop : class <Population> - 种群对象。
EndFlag : bool - 表示是否是最后一次调用该函数。
输出参数:
无输出参数。
"""
if not EndFlag:
self.passTime += time.time() - self.timeSlot # 更新用时记录，不计算画图的耗时
# 绘制动画
if self.drawing == 2:
metric = np.array(self.trace['f_best']).reshape(-1, 1)
self.ax = ea.soeaplot(metric,
Label='Objective Value',
saveFlag=False,
ax=self.ax,
gen=self.currentGen,
gridFlag=False) # 绘制动态图
elif self.drawing == 3:
self.ax = ea.varplot(pop.Phen,
Label='decision variables',
saveFlag=False,
ax=self.ax,
gen=self.currentGen,
gridFlag=False)
self.timeSlot = time.time() # 更新时间戳
else:
# 绘制最终结果图
if self.drawing != 0:
metric = np.vstack([self.trace['f_avg'],
self.trace['f_best']]).T
ea.trcplot(metric, [['种群个体平均目标函数值', '种群最优个体目标函数值']],
xlabels=[['Number of Generation']],
ylabels=[['Value']],
gridFlags=[[False]])
def stat(self, population): # 分析记录，更新进化记录器，population为传入的种群对象
# 进行进化记录
feasible = np.where(
np.all(population.CV <= 0,
1))[0] if population.CV is not None else np.arange(
population.sizes) # 找到可行解个体的下标
if len(feasible) > 0:
tempPop = population[feasible]
bestIdx = np.argmax(tempPop.FitnV) # 获取最优个体的下标
self.obj_trace[self.currentGen, 0] = np.sum(
tempPop.ObjV) / tempPop.sizes # 记录种群个体平均目标函数值
self.obj_trace[self.currentGen,
1] = tempPop.ObjV[bestIdx] # 记录当代目标函数的最优值
self.var_trace[self.currentGen, :] = tempPop.Phen[
bestIdx, :] # 记录当代最优的决策变量值
self.forgetCount = 0 # “遗忘策略”计数器清零
if np.abs(self.preObjV -
self.obj_trace[self.currentGen, 1]) < self.trappedValue:
self.trappedCount += 1
else:
self.trappedCount = 0 # 重置进化停滞计数器
self.passTime += time.time() - self.timeSlot # 更新用时记录
if self.drawing == 2:
self.ax = ea.soeaplot(self.obj_trace[:, [1]],
Label='Objective Value',
saveFlag=False,
ax=self.ax,
gen=self.currentGen,
gridFlag=False) # 绘制动态图
elif self.drawing == 3:
self.ax = ea.varplot(tempPop.Phen,
Label='decision variables',
saveFlag=False,
ax=self.ax,
gen=self.currentGen,
gridFlag=False)
self.timeSlot = time.time() # 更新时间戳
else:
self.currentGen -= 1 # 忽略这一代
self.forgetCount += 1 # “遗忘策略”计数器加1
def stat(self, pop): # 分析记录，更新进化记录器
# 进行进化记录
feasible = np.where(np.all(pop.CV <= 0, 1))[0] # 找到可行解个体的下标
if len(feasible) > 0:
tempPop = pop[feasible]
bestIdx = np.argmax(tempPop.FitnV) # 获取最优个体的下标
self.obj_trace[self.currentGen, 0] = np.sum(
tempPop.ObjV) / tempPop.sizes # 记录种群个体平均目标函数值
self.obj_trace[self.currentGen,
1] = tempPop.ObjV[bestIdx] # 记录当代目标函数的最优值
self.var_trace[self.currentGen, :] = tempPop.Phen[
bestIdx, :] # 记录当代最优的决策变量值
self.forgetCount = 0 # “遗忘策略”计数器清零
self.passTime += time.time() - self.timeSlot # 更新用时记录
if self.drawing == 2:
self.ax = ea.soeaplot(self.obj_trace[:, [1]], None, False,
self.ax, self.currentGen) # 绘制动态图
elif self.drawing == 3:
self.ax = ea.varplot(tempPop.Phen, 'decision variables', False,
self.ax, self.currentGen)
self.timeSlot = time.time() # 更新时间戳
else:
self.currentGen -= 1 # 忽略这一代
self.forgetCount += 1 # “遗忘策略”计数器加1
