72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
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# -*- coding: utf-8 -*-
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"""Demo.
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min f1 = -25 * (x1 - 2)**2 - (x2 - 2)**2 - (x3 - 1)**2 - (x4 - 4)**2 - (x5 - 1)**2
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min f2 = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x4 - 1)**2 + (x5 - 1)**2
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s.t.
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x1 + x2 >= 2
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x1 + x2 <= 6
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x1 - x2 >= -2
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x1 - 3*x2 <= 2
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4 - (x3 - 3)**2 - x4 >= 0
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(x5 - 3)**2 + x4 - 4 >= 0
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x1,x2,x3,x4,x5 ∈ {0,1,2,3,4,5,6,7,8,9,10}
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"""
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import numpy as np
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import geatpy as ea
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class MyProblem(ea.Problem): # 继承Problem父类
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def __init__(self, M=2):
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name = 'MyProblem' # 初始化name（函数名称，可以随意设置）
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Dim = 5 # 初始化Dim（决策变量维数）
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maxormins = [1] * M # 初始化maxormins（目标最小最大化标记列表，1：最小化该目标；-1：最大化该目标）
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varTypes = [1] * Dim # 初始化varTypes（决策变量的类型，0：实数；1：整数）
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lb = [0] * Dim # 决策变量下界
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ub = [10] * Dim # 决策变量上界
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lbin = [1] * Dim # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）
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ubin = [1] * Dim # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）
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# 调用父类构造方法完成实例化
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ea.Problem.__init__(self,
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name,
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M,
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maxormins,
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Dim,
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varTypes,
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lb,
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ub,
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lbin,
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ubin)
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def evalVars(self, Vars): # 目标函数
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x1 = Vars[:, [0]]
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x2 = Vars[:, [1]]
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x3 = Vars[:, [2]]
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x4 = Vars[:, [3]]
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x5 = Vars[:, [4]]
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f1 = -25 * (x1 - 2)**2 - (x2 - 2)**2 - (x3 - 1)**2 - (x4 - 4)**2 - (
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x5 - 1)**2
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f2 = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x4 - 1)**2 + (x5
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- 1)**2
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# # 利用罚函数法处理约束条件
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# idx1 = np.where(x1 + x2 < 2)[0]
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# idx2 = np.where(x1 + x2 > 6)[0]
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# idx3 = np.where(x1 - x2 < -2)[0]
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# idx4 = np.where(x1 - 3*x2 > 2)[0]
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# idx5 = np.where(4 - (x3 - 3)**2 - x4 < 0)[0]
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# idx6 = np.where((x5 - 3)**2 + x4 - 4 < 0)[0]
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# exIdx = np.unique(np.hstack([idx1, idx2, idx3, idx4, idx5, idx6])) # 得到非可行解的下标
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# f1[exIdx] = f1[exIdx] + np.max(f1) - np.min(f1)
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# f2[exIdx] = f2[exIdx] + np.max(f2) - np.min(f2)
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# 利用可行性法则处理约束条件
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CV = np.hstack([
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2 - x1 - x2,
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x1 + x2 - 6,
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-2 - x1 + x2,
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x1 - 3 * x2 - 2, (x3 - 3)**2 + x4 - 4,
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4 - (x5 - 3)**2 - x4
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])
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f = np.hstack([f1, f2])
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return f, CV
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