Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Table 5

The comparison results of the three algorithms on the 100-dimensional benchmark functions.

Function	Algorithm	Best	Worst	Mean	Std
	MBO	2.9655e-04	873.4768	483.6526	327.2392
	GCMBO	1.6826e-11	200.0985	10.0288	40.1674
	OPMBO	1.4014e-08	138.6132	4.8784	25.2979
	MBO	0.0082	1.8812e+03	942.8625	653.6147
	GCMBO	4.6673e-05	552.4148	139.0795	154.2869
	OPMBO	2.7599e-23	301.3835	15.2540	59.7389
	MBO	326.4544	8.2019e+05	3.6314e+05	1.7811e+05
	GCMBO	4.5214e+03	2.4929e+05	1.1272e+05	6.3874e+04
	OPMBO	3.8963e-08	1.4750e+05	9.4295e+03	2.8488e+04
	MBO	2.3891	90.9853	38.9181	25.9349
	GCMBO	0.7494	90.9000	40.2048	27.9953
	OPMBO	1.0715e-05	33.5396	7.8364	9.4976
	MBO	0.0807	484.7000	266.4036	175.1478
	GCMBO	5.0299e-07	250.5469	56.1723	71.6008
	OPMBO	5.4517e-05	2.2668	0.0760	0.4138
	MBO	433	288070	1.7473e+05	1.0058e+05
	GCMBO	0	106666	1.7420e+04	2.8532e+04
	OPMBO	0	3	0.1000	0.5477
	MBO	0.1632	4.2320e+04	1.3569e+04	1.4322e+04
	GCMBO	1.0042e-08	8.8990e+03	1.0016e+03	2.0665e+03
	OPMBO	1.9180e-05	6.8818e+03	432.4594	1.2535e+03
	MBO	4.5486e-11	2.9628e+09	8.5431e+08	1.1962e+09
	GCMBO	1.1964e-19	3.6385e+08	4.6229e+07	9.5275e+07
	OPMBO	8.3732e-13	5.9931e-12	2.9010e-12	1.4209e-12
	MBO	0.9237	5.0861e+09	1.4982e+09	1.7699e+09
	GCMBO	3.2011e-15	9.8268e+08	1.3463e+08	2.7277e+08
	OPMBO	9.2390e-12	0.4056	0.0135	0.0741
	MBO	3.7907	20.8227	18.6499	3.9959
	GCMBO	5.7380e-07	19.9186	8.6313	9.1342
	OPMBO	4.5998e-05	1.0344	0.0393	0.1897
	MBO	8.2058e+03	3.6580e+04	2.3230e+04	8.9660e+03
	GCMBO	0.0013	1.8954e+04	9.3635e+03	5.8928e+03
	OPMBO	0.0013	3.1905e+04	6.5790e+03	1.0764e+04
	MBO	262.6423	1.8211e+03	1.4277e+03	422.1270
	GCMBO	7.4610e-09	670.2994	113.0140	155.9463
	OPMBO	2.1383e-06	665.6871	57.3632	152.5890