Research Article
Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation
Table 6
The two-tailed t-test of the experimental results on 100-dimensional benchmark functions.
| Function | OPMBO with MBO | OPMBO with GCMBO | t | p | t | p |
| f1 | 791.0854 | 0 | 8.8112 | 1.4443e-18 | f2 | 946.4590 | 0 | 112.1480 | 0 | f3 | 692.0171 | 0 | 165.3924 | 0 | f4 | 395.9505 | 0 | 431.6215 | 0 | f5 | 938.4122 | 0 | 157.6091 | 0 | f6 | 1.2251e+03 | 0 | 97.7934 | 0 | f7 | 353.7764 | 0 | 15.2470 | 6.6025e-52 | f8 | 590.1854 | 0 | 32.2386 | 5.0606e-217 | f9 | 546.0571 | 0 | 48.7033 | 0 | f10 | 715.0020 | 0 | 272.3707 | 0 | f11 | 298.9920 | 0 | 16.7654 | 3.0813e-62 | f12 | 618.5786 | 0 | 10.6642 | 2.0751e-26 |
| Better | 12 | | 12 | | Equal | 0 | | 0 | | Worse | 0 | | 0 | |
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