# -*- coding: utf-8 -*-
import geatpy as ea # import geatpy
"""================================实例化问题对象=========================="""
problemName = 'WFG1' # 问题名称
fileName = problemName # 这里因为目标函数写在与之同名的文件里，所以文件名也是问题名称
MyProblem = getattr(__import__(fileName), problemName) # 获得自定义问题类
problem = MyProblem(3) # 生成问题对象
"""==================================种群设置=============================="""
Encoding = 'RI' # 编码方式
NIND = 100 # 种群规模
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders) # 创建区域描述器
population = ea.Population(Encoding, Field, NIND) # 实例化种群对象（此时种群还没被初始化，仅仅是完成种群对象的实例化）
"""================================算法参数设置============================"""
myAlgorithm = ea.moea_NSGA3_templet(problem, population) # 实例化一个算法模板对象
myAlgorithm.MAXGEN = 500 # 最大进化代数
myAlgorithm.drawing = 1 # 设置绘图方式（0：不绘图；1：绘制结果图；2：绘制目标空间过程动画；3：绘制决策空间过程动画）
"""===========================调用算法模板进行种群进化=========================
调用run执行算法模板，得到帕累托最优解集NDSet。NDSet是一个种群类Population的对象。
NDSet.ObjV为最优解个体的目标函数值；NDSet.Phen为对应的决策变量值。
详见Population.py中关于种群类的定义。
"""
NDSet = myAlgorithm.run() # 执行算法模板，得到非支配种群
NDSet.save() # 把结果保存到文件中
# 输出
print('用时：%f 秒'%(myAlgorithm.passTime))
print('评价次数：%d 次'%(myAlgorithm.evalsNum))
print('非支配个体数：%d 个'%(NDSet.sizes))
print('单位时间找到帕累托前沿点个数：%d 个'%(int(NDSet.sizes // myAlgorithm.passTime)))
# 计算指标
PF = problem.getBest() # 获取真实前沿，详见Problem.py中关于Problem类的定义
def optimize(self):
"""===============================实例化问题对象==========================="""
problem = MyProblem(self.M, self.maxormins, self.Dim, self.varTypes,
self.lb, self.ub, self.lbin, self.ubin,
self.f_list) # 生成问题对象
"""=================================种群设置==============================="""
Encoding = 'BG' # 编码方式,使用二进制/格雷编码
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges,
problem.borders) # 创建区域描述器
population = ea.Population(
Encoding, Field, self.NIND) # 实例化种群对象（此时种群还没被初始化，仅仅是完成种群对象的实例化）
"""===============================算法参数设置============================="""
if self.M == 1:
myAlgorithm = ea.soea_SGA_templet(
problem, population) # 实例化一个算法模板对象, single objective
else:
myAlgorithm = ea.moea_NSGA3_templet(
problem, population) # 实例化一个算法模板对象, multi objective
myAlgorithm.MAXGEN = self.MAXGEN # 最大进化代数
myAlgorithm.recOper.XOVR = 0.7 # 交叉概率
myAlgorithm.mutOper.Pm = 1 # 变异概率(换算成标准变异概率为0.025)
myAlgorithm.drawing = 0 # 设置绘图方式（0：不绘图；1：绘制结果图；2：绘制目标空间过程动画；3：绘制决策空间过程动画）
"""==========================调用算法模板进行种群进化======================="""
if self.M == 1: # single objective
[population, obj_trace, var_trace] = myAlgorithm.run() # 执行算法模板
self.obj_trace = obj_trace # save obj_trace for plot
population.save() # 把最后一代种群的信息保存到文件中
# 输出结果
best_gen = np.argmin(problem.maxormins *
obj_trace[:, 1]) # 记录最优种群个体是在哪一代
best_ObjV = np.array([obj_trace[best_gen, 1]])
best_var = np.array([var_trace[best_gen]])
if os.path.exists('best_ObjV.csv') == False:
np.savetxt('best_ObjV.csv', best_ObjV, delimiter=',')
np.savetxt('best_var.csv', best_var, delimiter=',')
else:
best_ObjV_old = self.read_csv_to_np('best_ObjV.csv')
best_var_old = self.read_csv_to_np('best_var.csv')
best_ObjV = np.vstack((best_ObjV_old, best_ObjV))
best_var = np.vstack((best_var_old, best_var))
np.savetxt('best_ObjV.csv', best_ObjV, delimiter=',')
np.savetxt('best_var.csv', best_var, delimiter=',')
# print('最优的目标函数值为：%s' % (best_ObjV))
# print('最优的控制变量值为：')
# for i in range(var_trace.shape[1]):
# print(var_trace[best_gen, i])
# print('有效进化代数：%s' % (obj_trace.shape[0]))
# print('最优的一代是第 %s 代' % (best_gen + 1))
# print('评价次数：%s' % (myAlgorithm.evalsNum))
# print('时间已过 %s 秒' % (myAlgorithm.passTime))
return population
else: # multi objective
NDSet = myAlgorithm.run() # 执行算法模板，得到帕累托最优解集NDSet
NDSet.save() # 把结果保存到文件中
# 输出
# print('用时：%s 秒' % (myAlgorithm.passTime))
# print('非支配个体数：%s 个' % (NDSet.sizes))
# print('单位时间找到帕累托前沿点个数：%s 个' % (int(NDSet.sizes // myAlgorithm.passTime)))
return NDSet
import geatpy as ea # Import geatpy
from myproblem import MyProblem # Import MyProblem class
if __name__ == '__main__':
"""=========================Instantiate your problem=========================="""
M = 3 # Set the number of objects.
problem = MyProblem(M) # Instantiate MyProblem class
"""===============================Population set=============================="""
Encoding = 'RI' # Encoding type.
NIND = 100 # Set the number of individuals.
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges,
problem.borders) # Create the field descriptor.
population = ea.Population(
Encoding, Field, NIND
) # Instantiate Population class(Just instantiate, not initialize the population yet.)
"""================================Algorithm set==============================="""
myAlgorithm = ea.moea_NSGA3_templet(
problem, population) # Instantiate a algorithm class.
myAlgorithm.MAXGEN = 500 # Set the max times of iteration.
"""===============================Start evolution=============================="""
NDSet = myAlgorithm.run() # Run the algorithm templet.
"""=============================Analyze the result============================="""
PF = problem.getReferObjV() # Get the global pareto front.
GD = ea.indicator.GD(NDSet.ObjV, PF) # Calculate GD
IGD = ea.indicator.IGD(NDSet.ObjV, PF) # Calculate IGD
HV = ea.indicator.HV(NDSet.ObjV, PF) # Calculate HV
Space = ea.indicator.Spacing(NDSet.ObjV) # Calculate Space
print('The number of non-dominated result: %s' % (NDSet.sizes))
print('GD: ', GD)
print('IGD: ', IGD)
print('HV: ', HV)
print('Space: ', Space)
def __init__(self,
X,
y,
target,
model,
l,
u,
drawing=1,
maxgen=100,
moea=1):
'''===============================实例化问题对象=================================='''
self.problem = MyProblem(target=target,
X=X,
y=y,
model=model,
l=l,
u=u)
'''===========================种群设置=============================='''
Encoding = 'RI' # 'RI':实整数编码，即实数和整数的混合编码；
NIND = 100 # 种群规模
Field = ea.crtfld(Encoding, self.problem.varTypes, self.problem.ranges,
self.problem.borders) # 创建区域描述器
self.population = ea.Population(Encoding, Field, NIND)
'''实例化种群对象（此时种群还没被真正初始化，仅仅是生成一个种群对象）'''
"""=========================算法参数设置============================"""
if moea == 1:
self.myAlgorithm = ea.moea_NSGA2_templet(
self.problem, self.population) # 实例化一个算法模板对象
elif moea == 2:
self.myAlgorithm = ea.moea_NSGA2_DE_templet(
self.problem, self.population)
elif moea == 3:
self.myAlgorithm = moea_NSGA2_toZero(self.problem, self.population)
elif moea == 4:
self.myAlgorithm = moea_NSGA2_DE_toZero(self.problem,
self.population)
elif moea == 5:
self.myAlgorithm = ea.moea_NSGA3_templet(self.problem,
self.population)
elif moea == 6:
self.myAlgorithm = ea.moea_NSGA3_DE_templet(
self.problem, self.population)
elif moea == 7:
self.myAlgorithm = ea.moea_awGA_templet(self.problem,
self.population)
elif moea == 8:
self.myAlgorithm = ea.moea_RVEA_templet(self.problem,
self.population)
elif moea == 9:
self.myAlgorithm = moea_NSGA2_10p_toZero(self.problem,
self.population)
elif moea == 10:
self.myAlgorithm = moea_NSGA2_20p_toZero(self.problem,
self.population)
elif moea == 11:
self.myAlgorithm = moea_NSGA2_30p_toZero(self.problem,
self.population)
elif moea == 12:
self.myAlgorithm = moea_NSGA2_random10p_toZero(
self.problem, self.population)
elif moea == 13:
self.myAlgorithm = moea_NSGA2_random20p_toZero(
self.problem, self.population)
elif moea == 14:
self.myAlgorithm = moea_NSGA2_random30p_toZero(
self.problem, self.population)
else:
print('error moea method!!')
self.myAlgorithm.MAXGEN = maxgen # 设置最大遗传代数
self.myAlgorithm.drawing = drawing
