Open AccessUsing the iCC framework for solving unconstrained LSGO problems
Author(s) -
Aleksei Vakhnin,
Evgenii Sopov
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012085
Subject(s) - metaheuristic , curse of dimensionality , mathematical optimization , benchmark (surveying) , coevolution , evolutionary algorithm , computer science , evolutionary computation , optimization problem , wilcoxon signed rank test , computational intelligence , rank (graph theory) , mathematics , artificial intelligence , statistics , paleontology , geodesy , mann–whitney u test , biology , geography , combinatorics
Evolutionary computation is a branch of computational intelligence that is inspired by nature. This algorithm family uses fundamental rules of natural selection to find a better solution when solving optimization problems. The productivity of these methods decreases when the number of parameters to be optimized is extra-large. This area of research is known as large-scale global optimization. Cooperative coevolution is a structure for evolutionary algorithms which tries to solve the curse of dimensionality problem. The cooperative coevolution efficiency of application largely depends on the two settings: the size of each subcomponent(subproblem) and the grouping of variables. The actual work proposes improved cooperative coevolution denoted as iCC. The iCC approach dynamically resizes the groups. iCC starts with a predefined set of subproblems and reduces them gradually during the optimization process. A novel metaheuristic has been developed which is called iCC-SHADE for black-box optimization problems with a large number of variables. The proposed method has been tested on fifteen optimization tasks from the LSGO CEC’2013 competition benchmark. The experimental results have demonstrated that iCC-SHADE has statistically better performance than CC-SHADE with a static number of subproblems. Also, the effectiveness of iCC-SHADE has been tested in comparison with other modern metaheuristics. The Wilcoxon rank-sum test was used to compare the effectiveness of investigated metaheuristics.