Тип публикации: статья из журнала
Год издания: 2021
Идентификатор DOI: 10.3390/a14050146
Ключевые слова: computational intelligence, cooperative coevolution, evolutionary algorithms, evolutionary computation, large-scale global optimization
Аннотация: Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for perПоказать полностьюforming the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCCSHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Журнал: Algorithms
Выпуск журнала: Vol. 14, Is. 5
Номера страниц: 146
ISSN журнала: 19994893
Издатель: MDPI AG