Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023); Krasnoyarsk; Krasnoyarsk

Год издания: 2024

Идентификатор DOI: 10.1051/itmconf/20245902022

Аннотация: Solving constrained large-scale global optimization problems poses a challenging task. In these problems with constraints, when the number of variables is measured in the thousands, when the constraints are presented in the form of a black box, and neither the size nor the configuration of the feasible region is known, it is very difficult to find at least one feasible solution. In general, such a problem of finding a feasible region is known as a constraint satisfaction problem. In this paper, we have extended a well-known benchmark set based on constrained optimization problems up to 1000 variables. We have evaluated the CC-SHADE performance, to tackle constraints in large-scale search space. CC-SHADE merges the power of cooperative coevolution and self-adaptive differential evolution. Our extensive experimental evaluations on a range of benchmark problems demonstrate the strong dependence of the performance of CC-SHADE on the number of individuals and the subcomponent number. The numerical results emphasize the importance of using a cooperative coevolution framework for evolutionary-based approaches compared to conventional methods. All numerical experiments are proven by the Wilcoxon test.

Журнал: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023)

Номера страниц: 2022

Место издания: Krasnoyarsk

• Vakhnin Aleksei (Reshetnev Siberian State University of Science and Technology, Institute of Informatics and Telecommunications)
• Novikov Zakhar (Siberian Federal University, Institute of Space and Information Technology)

• РИНЦ (eLIBRARY.RU)