import numpy as np
import geatpy as ea
import random
data=[100,50,50,150,150,20,20,20,15,15,25,25,20,80,50,50,200,50,150,40,150,20,40,20,100,40,40,100,50,50]
drop=[79879.56,308.4,1622.16,25522.99,25437.74,15.11,192.49,949.76,233.68,289.97,213.47,1064.9,425.72,8383.93,8845.67,1093.5,345.32,115.44,
5244.92,126937.86,412.26,71185.43,13.25,232.44,116.78,23453.1,5085.09,171.56,905.92,5478,3808.46,228209.42]
water=random.sample(data,5)
rate=random.sample(drop,5)
list1=[]
for i in range(len(drop)):
for j in range(5):
if rate[j]==drop[i]:
list1.append(i)
print(rate)
class MyProblem(ea.Problem):
def __init__(self):
name = ' '
M = 2
maxormins = [-1] * M
Dim = 5
varTypes = [0] * Dim
lb = [0] * Dim
ub = [1] * Dim
lbin = [0] * Dim
ubin = [1] * Dim
ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
def aimFunc(self, pop):
Vars = pop.Phen
ObjV1 = -(5*180+2*(data[list1[0]]+data[list1[1]]+data[list1[2]]+data[list1[3]]+data[list1[4]])-((1-Vars[:, 0])*rate[0]*0.05*3+(1-Vars[:,1])*rate[1]*0.05*3+(1-Vars[:,2])*rate[2]*0.05*3+(1-Vars[:,3])*rate[3]*0.05*3+(1-Vars[:,4])*rate[4]*0.05*3)*2)
ObjV2 =(Vars[:,0]+Vars[:,1]+Vars[:,2]+Vars[:,3]+Vars[:,4])*0.2
pop.ObjV = np.array([ObjV1, ObjV2]).T
print(pop.ObjV)
print('----------------------')
print(Vars[:,0])
print('----------------------')
print(Vars[:, 1])
print('----------------------')
print(Vars[:, 2])
print('----------------------')
print(Vars[:, 3])
print('----------------------')
print(Vars[:, 4])
print('---------------------')
"""================================实例化问题对象============================="""
problem = MyProblem()
"""==================================种群设置================================"""
Encoding = 'RI'
NIND = 100
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders)
population = ea.Population(Encoding, Field, NIND)
"""================================算法参数设置==============================="""
myAlgorithm = ea.moea_NSGA2_templet(problem, population)
myAlgorithm.MAXGEN = 300
myAlgorithm.logTras = 1
myAlgorithm.verbose = True
myAlgorithm.drawing = 1
"""==========================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以及最后一代种群。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。
"""
[NDSet, population] = myAlgorithm.run()
NDSet.save()
"""==================================输出结果=============================="""
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) if NDSet.sizes != 0 else print('没有找到可行解!')
if myAlgorithm.log is not None and NDSet.sizes != 0:
print('GD', myAlgorithm.log['gd'][-1])
print('IGD', myAlgorithm.log['igd'][-1])
print('HV', myAlgorithm.log['hv'][-1])
print('Spacing', myAlgorithm.log['spacing'][-1])
"""=========================进化过程指标追踪分析========================="""
metricName = [['igd'], ['hv']]
Metrics = np.array([myAlgorithm.log[metricName[i][0]] for i in range(len(metricName))]).T
ea.trcplot(Metrics, labels=metricName, titles=metricName)
