Библиографическое описание:Ranked Archive Differential Evolution with Selective Pressure for CEC 2020 Numerical Optimization* : доклад, тезисы доклада / V. Stanovov, S. Akhmedova, E. Semenkin. - [S. l. : s. n.], 2020. - Текст : непосредственный // 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings / 2020 IEEE Congress on Evolutionary Computation, CEC 2020 (2020 ; 19.07 - 24.07 ; Virtual, Glasgow) : Institute of Electrical and Electronics Engineers Inc., 2020. - P. 9185614. - DOI 10.1109/CEC48606.2020.9185614.
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.
