Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

Table 6

Results of multiple-problem Wilcoxon’s test for ANDE, ANDE-1, and ANDE-2 versus SDENS, jDElsgo, DECC-DML, DASA, EOEA, MA-SW-CHAINS, and DMS-PSO-SHS over all functions at a significance level 0.05 (with 3.00E + 06 FEs).

Algorithm			value	Better	Equal	Worse	Dec.
ANDE versus SDENS	187	23	0.002	17	0	3	+
ANDE versus jDElsgo	31	179	0.006	4	0	16	−
ANDE versus DECC-DML	185	25	0.003	16	0	4	+
ANDE versus DASA	103	107	0.940	12	0	8	≈
ANDE versus EOEA	88	122	0.526	9	0	11	≈
ANDE versus MA-SW-CHAINS	66	144	0.145	8	0	12	≈
ANDE versus DMS-PSO-SHS	54	156	0.057	7	0	13	≈
ANDE-1 versus SDENS	187	23	0.002	17	0	3	+
ANDE-1 versus jDElsgo	50	160	0.040	5	0	15	−
ANDE-1 versus DECC-DML	164	26	0.005	13	1	6	+
ANDE-1 versus DASA	122	88	0.526	12	0	8	≈
ANDE-1 versus EOEA	100	110	0.852	9	0	11	≈
ANDE-1 versus MA-SW-CHAINS	85	125	0.455	9	0	11	≈
ANDE-1 versus DMS-PSO-SHS	87	123	0.502	8	0	12	≈
ANDE-2 versus SDENS	155	55	0.062	15	0	5	≈
ANDE-2 versus jDElsgo	19	191	0.001	4	0	16	−
ANDE-2 versus DECC-DML	131	79	0.332	10	0	10	≈
ANDE-2 versus DASA	64	146	0.126	8	0	12	≈
ANDE-2 versus EOEA	41	169	0.017	5	0	15	−
ANDE-2 versus MA-SW-CHAINS	40	170	0.015	5	0	15	−
ANDE-2 versus DMS-PSO-SHS	21	189	0.002	16	0	4	−