Description
I have prepared python code for the Binary Multi-Neighborhood Artificial Bee Colony algorithm.
Beheshti, Zahra. “BMNABC: Binary Multi-Neighborhood Artificial Bee Colony for High-Dimensional Discrete Optimization Problems.” Cybernetics and Systems 49.7-8 (2018): 452-474.
The cost function in this code is a machine learning model (KNN classifier). BMNABC try to find the best feature to gain high accuracy.
Result :
Iteration:1 Accuracy:75.0 Fitness: 0.25287037037037036 Selected Features: 147
Iteration:1 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:2 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:3 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:4 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:5 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:6 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:7 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:8 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:9 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:10 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:11 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:12 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:13 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:14 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:15 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:16 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:17 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:18 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 129
Iteration:19 Accuracy:75.33333333333333 Fitness: 0.24949629629629633 Selected Features: 145
Iteration:20 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:21 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:22 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:23 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:24 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:25 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:26 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:27 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:28 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:29 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:30 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:31 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:32 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:33 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:34 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:35 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:36 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:37 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:38 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:39 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:40 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:41 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:42 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:43 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:44 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:45 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:46 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:47 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:48 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:49 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:50 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:51 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:52 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:53 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:54 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:55 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:56 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:57 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:58 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:59 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:60 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:61 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:62 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:63 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:64 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:65 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:66 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:67 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:68 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:69 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:70 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:71 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:72 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:73 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:74 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:75 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:76 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:77 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:78 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:79 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:80 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:81 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:82 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:83 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:84 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:85 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:86 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:87 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:88 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:89 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:90 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:91 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:92 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:93 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:94 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:95 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:96 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:97 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 246
Iteration:98 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:99 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
Iteration:100 Accuracy:76.0 Fitness: 0.24663703703703702 Selected Features: 28
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