Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: 2nd International Scientific Conference on Metrological Support of Innovative Technologies, ICMSIT 2020
Год издания: 2020
Идентификатор DOI: 10.1088/1742-6596/1515/3/032076
Аннотация: Large-scale global optimization (LSGO) problems are emergent in many domains of applied sciences. LSGO is a hard challenge for the majority of state-of-the-art optimization methods. Evolution algorithms (EAs) combined with Cooperative Coevolution (CC) are able to perform well when solving many real-world LSGO problems. The grouping of variables at the problem decomposition stage has a significant impact on the performance of the CC approach. This study proposes an improvement of the previously developed random adaptive grouping (RAG) approach for CC. The new method is titled as RAG2, and the whole optimization algorithm, based on RAG2, is called CC-SHADE-RAG2. The influence of the choice of the population size and the number of subcomponents on the algorithm performance have been investigated using the IEEE LSGO CEC'2013 benchmark. The set of test problems in the benchmark contains fifteen functions with dimensionality equal to one thousand. We have also compared the performance of the novel algorithm with some EAs, which are applied for solving LSGO problems. © 2020 Published under licence by IOP Publishing Ltd.
Журнал: Journal of Physics: Conference Series
Выпуск журнала: Vol. 1515, Is. 3
ISSN журнала: 17426588
Издатель: Institute of Physics Publishing32076
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