1 | #pip install geneticalgorithm |
2 | import numpy as np |
3 | from geneticalgorithm import geneticalgorithm as ga |
4 | # Define charateristics of variables: |
5 | varbound=np.array([[0,1],[0,1]]) |
6 | vartype=np.array([['real'],['real']]) |
7 |
8 | # Define settings of the algorithm: |
9 | algorithm_param = {'max_num_iteration': 100,\ |
10 | 'population_size':60,\ |
11 | 'mutation_probability':0.1,\ |
12 | 'elit_ratio': 0.01,\ |
13 | 'crossover_probability': 0.5,\ |
14 | 'parents_portion': 0.3,\ |
15 | 'crossover_type':'uniform',\ |
16 | 'max_iteration_without_improv':None} |
17 | |
18 | # Define your optimization model: |
19 | def MyOptProb(X): |
20 | y = 0+X[1]*(1.29-0) |
21 | x = np.round(0+X[0]*(2-0)) |
22 | g1 = 5/10 * x + 3/10 * y - 1 |
23 | g2 = 2/9 * x + 7/9 * y - 1 |
24 | penalty = np.amax(np.array([0,g1,g2])) |
25 | return -(2*x+5*y)+150*penalty**2 |
26 |
27 | # Define a solution retriever: |
28 | def Results(obj, variables): |
29 | x = round(0+variables[0]*(2-0)) |
30 | y = 0+variables[0]*(1.29-0) |
31 | g1 = 5 * x + 3 * y - 10 |
32 | g2 = 2 * x + 7 * y - 9 |
33 | print(g1) |
34 | print(g2) |
35 | if g1>10e-6 or g2>10e-6: |
36 | print("infeasible") |
37 | else: |
38 | print("feasible") |
39 | print("x:",x) |
40 | print("y:",y) |
41 | print("obj:",2*x+5*y) |
42 |
43 | # Model and solve the problem: |
44 | model=ga(function=MyOptProb,dimension=2, |
45 | variable_type_mixed=vartype,variable_boundaries=varbound, |
46 | algorithm_parameters=algorithm_param) |
47 | model.run() |
48 | # Display results: |
49 | Results(model.best_function,model.best_variable) |
50 | # The best solution found: |
51 | # [0.41141754 0.79674808] |
52 | # Objective function: |
53 | # -7.068871696566733 |
54 | # -3.40781412393091 |
55 | # -3.284899622505458 |
56 | # feasible |
57 | # x: 1 |
58 | # y: 0.5307286253563631 |
59 | # obj: 4.653643126781816 |
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