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

Table 3

The comparison results of the three algorithms on the 20-dimensional benchmark functions.

Function	Algorithm
	MBO	2.1817e-08	1056671	10.2897	24.1542
	GCMBO	1.4305e-12	2.2191e-08	4.0376e-09	6.1440e-09
	OPMBO	1.7124e-18	2.8958e-09	1.9961e-10	5.6832e-10
	MBO	2.0696e-05	45.8467	14.1370	14.5827
	GCMBO	8.5651e-31	2.7743	0.1784	0.5239
	OPMBO	1.1985e-32	1.9515e-25	9.6741e-27	3.6316e-26
	MBO	467.5414	2.6400e+04	9.9164e+03	7.0557e+03
	GCMBO	5.2744	1.1659e+04	5.7437e+03	3.6304e+03
	OPMBO	4.7218e-16	105.8061	12.9713	25.4996
	MBO	0.0656	69.6874	34.4202	23.9322
	GCMBO	0.8478	65	21.8416	18.9365
	OPMBO	6.4794e-08	9.7491	1.3394	2.5359
	MBO	2.4859e-04	76.0287	19.5472	24.7662
	GCMBO	0	18.8264	2.2732	5.8333
	OPMBO	1.2577e-10	6.6586e-06	1.7297e-06	1.8097e-06
	MBO	0	38189	8.2961e+03	1.3038e+04
	GCMBO	0	2463	9.72000	449.5788
	OPMBO	0	0	0	0
	MBO	3.2274e-06	3.0575e+03	636.4260	1.0172e+03
	GCMBO	1.7541e-07	342.4249	71.0668	96.0405
	OPMBO	1.7164e-05	18.6066	14.1968	7.9660
	MBO	6.8753e-12	3.3320e+08	4.1706e+07	8.7357e+07
	GCMBO	1.0427e-14	4.4128e+06	1.6156e+05	8.0584e+05
	OPMBO	1.5705e-32	1.0369e-12	7.8430e-14	2.4871e-13
	MBO	4.3862e-10	4.9682e+08	5.5076e+07	1.3686e+08
	GCMBO	2.3991e-14	1.1729e+07	7.9503e+05	2.5028e+06
	OPMBO	5.6159e-28	5.2997e-12	3.5047e-13	1.0677e-12
	MBO	5.0008e-04	20.1443	8.9263	7.9682
	GCMBO	1.9918e-06	5.5369	0.2610	1.0806
	OPMBO	1.6130e-10	1.6511e-05	6.9485e-06	5.0036e-06
	MBO	2.5817e-04	4.8952e+03	2.3067e+03	1.6744e+03
	GCMBO	2.5455e-04	3.0729e+03	1.1757e+03	1.0464e+03
	OPMBO	2.5455e-04	2.3009e+03	76.6964	420.0823
	MBO	1.1262e-04	255.8194	97.2892	96.4221
	GCMBO	1.9541e-08	74.1824	10.7346	15.3041
	OPMBO	0	2.4978e-07	2.9660e-08	5.3340e-08