# 变量设置
ranges = np.array([[-5, -5], [5, 5]]) # 生成自变量的范围矩阵
borders = np.array([[1, 1], [1, 1]]) # 生成自变量的边界矩阵（1表示变量的区间是闭区间）
precisions = [1, 1
] # 根据crtfld的函数特性，这里需要设置精度为任意正值，否则在生成区域描述器时会默认为整数编码，并对变量范围作出一定调整
FieldDR = ga.crtfld(ranges, borders, precisions) # 生成区域描述器
# 调用编程模板
[ObjV, NDSet, NDSetObjV, times] = ga.moea_nsga2_templet(AIM_M,
'aimfuc',
None,
None,
FieldDR,
'R',
maxormin=1,
MAXGEN=500,
MAXSIZE=200,
NIND=25,
SUBPOP=1,
GGAP=1,
selectStyle='tour',
recombinStyle='xovdp',
recopt=0.9,
pm=0.6,
distribute=True,
drawing=1)
print('其中一个最优解是', ObjV[0])
# 用时：3.41090 秒
# 帕累托前沿点个数：200 个
# 单位时间找到帕累托前沿点个数：58 个
# 其中一个最优解是 [ 90.081716 132.51084595]
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 # set any precision that is bigger than 0, or 'crtfld' would create a Field for integer-code.
FieldDR = ga.crtfld(ranges, borders, precisions) # create the FieldDR
"""=======================use sga2_templet to find the Pareto front==================="""
[ObjV, NDSet, NDSetObjV,
times] = ga.moea_nsga2_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)