In [1]:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | import geatpy as ea
import numpy as np
# 构建问题
r = 1 # 目标函数需要用到的额外数据
@ea.Problem.single
def evalVars(Vars): # 定义目标函数（含约束）
f = np.sum((Vars - r) ** 2) # 计算目标函数值
x1 = Vars[0]
x2 = Vars[1]
CV = np.array([(x1 - 0.5)**2 - 0.25,
(x2 - 1)**2 - 1]) # 计算违反约束程度
return f, CV
problem = ea.Problem(name='soea quick start demo',
M=1, # 目标维数
maxormins=[1], # 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
Dim=5, # 决策变量维数
varTypes=[0, 0, 1, 1, 1], # 决策变量的类型列表，0：实数；1：整数
lb=[-1, 1, 2, 1, 0], # 决策变量下界
ub=[1, 4, 5, 2, 1], # 决策变量上界
evalVars=evalVars)
# 构建算法
algorithm = ea.soea_SEGA_templet(problem,
ea.Population(Encoding='RI', NIND=20),
MAXGEN=50, # 最大进化代数。
logTras=1, # 表示每隔多少代记录一次日志信息，0表示不记录。
trappedValue=1e-6, # 单目标优化陷入停滞的判断阈值。
maxTrappedCount=10) # 进化停滞计数器最大上限值。
# 求解
res = ea.optimize(algorithm, seed=1, verbose=True, drawing=1, outputMsg=True, drawLog=False, saveFlag=True, dirName='result')
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================================================================================== gen| eval | f_opt | f_max | f_avg | f_min | f_std ---------------------------------------------------------------------------------- 0 | 20 | 9.18566E+00 | 9.18566E+00 | 9.18566E+00 | 9.18566E+00 | 0.00000E+00 1 | 40 | 5.78235E+00 | 9.18566E+00 | 8.33483E+00 | 5.78235E+00 | 1.47368E+00 2 | 60 | 5.78235E+00 | 1.01857E+01 | 8.44530E+00 | 5.78235E+00 | 1.56014E+00 3 | 80 | 5.18566E+00 | 9.18566E+00 | 7.10178E+00 | 5.18566E+00 | 1.70646E+00 4 | 100 | 2.18566E+00 | 5.78235E+00 | 5.31974E+00 | 2.18566E+00 | 1.00074E+00 5 | 120 | 2.18566E+00 | 5.71252E+00 | 4.47875E+00 | 2.18566E+00 | 1.38039E+00 6 | 140 | 2.18566E+00 | 5.18566E+00 | 2.88560E+00 | 2.18566E+00 | 1.09847E+00 7 | 160 | 2.02037E+00 | 2.18566E+00 | 2.15260E+00 | 2.02037E+00 | 6.61145E-02 8 | 180 | 2.00000E+00 | 2.18565E+00 | 2.02762E+00 | 2.00000E+00 | 3.65244E-02 9 | 200 | 2.00000E+00 | 2.02037E+00 | 2.01625E+00 | 2.00000E+00 | 8.12936E-03 10| 220 | 2.00000E+00 | 2.02037E+00 | 2.00908E+00 | 2.00000E+00 | 9.96122E-03 11| 240 | 1.00000E+00 | 2.00000E+00 | 1.95000E+00 | 1.00000E+00 | 2.17945E-01 12| 260 | 1.00000E+00 | 2.00000E+00 | 1.80000E+00 | 1.00000E+00 | 4.00000E-01 13| 280 | 1.00000E+00 | 2.00000E+00 | 1.45000E+00 | 1.00000E+00 | 4.97494E-01 14| 300 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 0.00000E+00 15| 320 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 0.00000E+00 16| 340 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 1.00000E+00 | 0.00000E+00
Execution time: 0.024933338165283203 s Evaluation number: 340 The best objective value is: 1.0 The best variables are: 1.0 1.0 2.0 1.0 1.0
