Statistical analysis of convergence performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS

Abstract

Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its convergence performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' convergence performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.