Сибирский федеральный университет

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

Конференция: IEEE Congress on Evolutionary Computation, CEC 2020; Virtual, Glasgow; Virtual, Glasgow

Год издания: 2020

Идентификатор DOI: 10.1109/CEC48606.2020.9185614

Ключевые слова: CEC 2020, differential evolution, optimization, selective pressure

Аннотация: The single-objective numerical optimization is an important research field due to variety of real world applications. One of the most promising classes of numerical optimization algorithms is Differential Evolution. This paper proposes a new algorithm called RASP-SHADE to solve the CEC 2020 Bound Constrained Single Objective Optimization benchmark problems. The developed algorithm is based on the L-SHADE with Distance-based success history adaptation, incudes parameter adaptations of the jSO algorithm, and introduces several novelties. The ranking of population and archive according to fitness introduces the selective pressure, resulting in a new mutation strategy. A new archive update rule is applied with replacing only worst points and the parameters sampling scheme is changed. The experiments show that RASP-SHADE modifications result in significant improvements when compared to other state-of-the-art algorithms.

Журнал: 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Номера страниц: 9185614

Издатель: Institute of Electrical and Electronics Engineers Inc.

• Stanovov V. (Institute of Informatics and Telecommunication,Reshetnev Siberian State University of Science and Technology)
• Akhmedova S. (Institute of Informatics and Telecommunication,Reshetnev Siberian State University of Science and Technology)
• Semenkin E. (Institute of Informatics and Telecommunication,Reshetnev Siberian State University of Science and Technology)

• Ядро РИНЦ (eLIBRARY.RU)