Сибирский федеральный университет

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 performing 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

• Vakhnin Aleksei (Reshetnev Siberian State Univ Sci & Technol, Dept Syst Anal & Operat Res, Krasnoyarsk 660037, Russia)
• Sopov Evgenii (Reshetnev Siberian State Univ Sci & Technol, Dept Syst Anal & Operat Res, Krasnoyarsk 660037, Russia; Siberian Fed Univ, Dept Informat Syst, Krasnoyarsk 660041, Russia)

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