An Improved Grey Wolf Optimization Algorithm with Variable Weights

Table 4

Statistical analysis on the absolute errors of the selected functions (dim = 2).

Functions	VM-GWO		Std. GWO		ALO		PSO		BA
	Mean	Std. deviation	Mean	Std. deviation	Mean	Std. deviation	Mean	Std. deviation	Mean	Std. deviation
F1	7.2039e − 66	3.5263e − 65	6.59E − 28	6.34E − 5 [27]	2.59E − 10	1.65E − 10 [2]	1.36E − 4	2.02E − 4 [27]	0.773622	0.528134 [2]
F2	1.3252e − 34	3.5002e − 34	7.18E − 17	0.02901 [27]	1.84241E − 6	6.58E − 7 [2]	0.042144	0.04542 [27]	0.334583	3.186022 [2]
F3	3.7918e − 60	1.1757e − 59	3.29E − 6	79.1496 [27]	6.0685E − 10	6.34E − 10 [2]	70.12562	22.1192 [27]	0.115303	0.766036 [2]
F4	2.2262e − 46	2.8758e − 46	5.61E − 7	1.31509 [27]	1.36061E − 8	1.81E − 9 [2]	0.31704	7.3549 [27]	0.192185	0.890266 [2]
F5	3.6015e − 131	9.0004e − 131	7.8319e − 97	2.4767e − 96	2.1459e − 20	2.8034e − 20	8.4327e − 20	1.7396e − 19	1.7314e − 17	4.9414e − 17
F9	0.0047	0.0040	0.00449	0.00666 [27]	0.0301	0.0329	0.00922	0.00772 [27]	0.0436	0.0294
F10	0.0200	0.0421	0.0499	0.0526	0.01860449	0.009545 [2]	0.273674	0.204348 [2]	1.451575	0.570309 [2]
F11	1.2999e − 60	4.1057e − 60	6.8181e − 35	1.5724e − 34	1.1562e − 13	1.2486e − 13	2.3956e − 12	3.6568e − 12	5.0662e − 09	4.9926e − 09