Table 40 Comparison of CI–SAPF and CI–SAPF–CBO for monotone test functions using Wilcoxon’s rank sum test at \(\alpha =0.05\)

From: Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems

  Test examples Monotone functions
1 2 3 4 5 6
Function values Test statistic 0 0.384395061 − 0.221766381 1.818484327 − 1.108831906 0
P value 0.5 0.64965717 0.412247876 0.96550493 0.133751352 0.5
h-value 0 0 0 0 0 0
CPU time Test statistic − 6.372087356 − 4.538818604 4.361405499 -0.066529914 4.405758775 − 0.35482621
P value 9.32363E-11 2.82851E-06 0.999993539 0.473477971 0.999994729 0.361359896
h-value 1 1 0 0 0 0
Function evaluations Test statistic 0.391787274 1.071870843 4.257914521 -0.066529914 4.213561244 3.836558396
P value 0.6523923 0.858110976 0.999989683 0.473477971 0.999987431 0.999937615
h-value 0 0 0 0 0 0
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