Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: III International Conference on Advanced Technologies in Aerospace, Mechanical and Automation Engineering - MIST: Aerospace-III-2020; 9-th International Workshop on Mathematical Models and their Applications (IWMMA-2020); Krasnoyarsk; Krasnoyarsk
Год издания: 2021
Идентификатор DOI: 10.1088/1757-899X/1047/1/012085
Аннотация: 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. ThisПоказать полностьюarea 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.
Журнал: IOP Conference Series: Materials Science and Engineering
Выпуск журнала: 1047
Номера страниц: 12085
Место издания: Krasnoyarsk, Russian Federation
Издатель: IOP Publishing Ltd