MAXGEN = 1000 # 最大遗传代数
GGAP = 1 # 代沟：子代与父代的重复率为(1-GGAP),由于后面使用NSGA2算法，因此该参数无用
selectStyle = 'tour' # 遗传算法的选择方式
recombinStyle = 'xovdprs' # 遗传算法的重组方式，设为使用代理的两点交叉
recopt = 0.9 # 交叉概率
pm = 0.1 # 变异概率
SUBPOP = 1 # 设置种群数为1
maxormin = 1 # 设置标记表明这是最小化目标
MAXSIZE = 1000 # 帕累托最优集最大个数
FieldDR = ga.crtfld(ranges, borders, precisions) # 生成区域描述器
"""=======================调用编程模板进行种群进化==================="""
# 得到帕累托最优解集NDSet以及解集对应的目标函数值NDSetObjV
[ObjV, NDSet, NDSetObjV, times] = ga.q_sorted_templet(AIM_M,
AIM_F,
None,
None,
FieldDR,
'R',
maxormin,
MAXGEN,
MAXSIZE,
NIND,
SUBPOP,
GGAP,
selectStyle,
recombinStyle,
recopt,
pm,
distribute=False,
drawing=1)
MAXGEN = 500 # 最大遗传代数
GGAP = 1
# 代沟：子代与父代的重复率为(1-GGAP)
selectStyle = 'tour' # 遗传算法的选择方式
recombinStyle = 'xovdprs' # 遗传算法的重组方式，设为两点交叉
recopt = 0.9 # 交叉概率
pm = None # 变异概率
SUBPOP = 1 # 设置种群数为1f
maxormin = 1 # 设置标记表明这是最小化目标
MAXSIZE = 1000 # 帕累托最优集最大个数
"""=======================调用编程模板进行种群进化==================="""
# 调用编程模板进行种群进化，得到种群进化和变量的追踪器以及运行时间
[ObjV, NDSet, times] = ga.q_sorted_templet(AIM_M,
AIM_F,
None,
None,
ranges,
borders,
precisions,
maxormin,
MAXGEN,
MAXSIZE,
NIND,
SUBPOP,
GGAP,
selectStyle,
recombinStyle,
recopt,
pm,
drawing=1)
"""sortedParetoGA_main.py"""
import numpy as np
import geatpy as ga # 导入geatpy库
# 获取函数接口地址
AIM_M = __import__('aimfuc')
"""============================变量设置============================"""
ranges = np.vstack([np.zeros((1, 6)), np.ones((1, 6))]) # 生成自变量的范围矩阵
borders = np.vstack([np.ones((1, 6)), np.ones((1, 6))]) # 生成自变量的边界矩阵
precisions = [4] * 30 # 自变量的编码精度
"""=======================调用编程模板进行种群进化==================="""
[ObjV, NDSet, times] = ga.q_sorted_templet(AIM_M,
'aimfuc',
None,
None,
ranges,
borders,
precisions,
maxormin=1,
MAXGEN=1000,
MAXSIZE=1000,
NIND=50,
SUBPOP=1,
GGAP=1,
selectStyle='tour',
recombinStyle='xovdprs',
recopt=0.9,
pm=None,
drawing=1)
AIM_M = __import__('aimfuc') # get the address of objective function
AIM_F = 'DTLZ1' # You can set DTL1,2,3 or 4
"""==================================variables setting================================"""
ranges = np.vstack([np.zeros((1, 7)), np.ones(
(1, 7))]) # define the ranges of variables in DTLZ1
borders = np.vstack([np.ones((1, 7)), np.ones(
(1, 7))]) # define the borders of variables in DTLZ1
precisions = [
4
] * 7 # define the precisions of variables, just set any values optionally that is bigger than 0 because variables are successive and we use real-code in the algorithm templet
FieldDR = ga.crtfld(ranges, borders) # create the FieldDR
"""=======================use sga2_templet to find the Pareto front==================="""
[ObjV, NDSet, NDSetObjV, times] = ga.q_sorted_templet(AIM_M,
AIM_F,
None,
None,
FieldDR,
problem='R',
maxormin=1,
MAXGEN=500,
MAXSIZE=2000,
NIND=50,
SUBPOP=1,
GGAP=1,
selectStyle='tour',
recombinStyle='xovdprs',
recopt=0.9,
pm=None,
distribute=False,
drawing=1)