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

Table 5

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 6.00E + 05 FEs).

Algorithm			value	Better	Equal	Worse	Dec.
ANDE versus SDENS	210	0	0.000	20	0	0	+
ANDE versus jDElsgo	192	18	0.001	18	0	2	+
ANDE versus DECC-DML	178	32	0.006	15	0	5	+
ANDE versus DASA	77	133	0.296	8	0	12	≈
ANDE versus EOEA	67	143	0.156	6	0	14	≈
ANDE versus MA-SW-CHAINS	38	172	0.012	1	0	15	+
ANDE versus DMS-PSO-SHS	78	132	0.313	8	0	12	≈
ANDE-1 versus SDENS	210	0	0.000	20	0	0	+
ANDE-1 versus jDElsgo	172	38	0.012	15	0	5	+
ANDE-1 versus DECC-DML	162	48	0.033	12	0	8	+
ANDE-1 versus DASA	87	123	0.502	8	0	12	≈
ANDE-1 versus EOEA	61	149	0.100	4	0	16	≈
ANDE-1 versus MA-SW-CHAINS	41	169	0.017	4	0	16	−
ANDE-1 versus DMS-PSO-SHS	93	117	0.654	9	0	11	≈
ANDE-2 versus SDENS	187	23	0.002	18	0	2	+
ANDE-2 versus jDElsgo	154	56	0.067	15	0	5	≈
ANDE-2 versus DECC-DML	154	56	0.067	14	0	6	≈
ANDE-2 versus DASA	60	150	0.093	7	0	13	≈
ANDE-2 versus EOEA	52	158	0.048	5	0	15	−
ANDE-2 versus MA-SW-CHAINS	43	167	0.021	6	0	14	−
ANDE-2 versus DMS-PSO-SHS	61	149	0.100	7	0	13	≈