Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments

Vladimir Stanovov, Shakhnaz Akhmedova, Aleksei Vakhnin, Evgenii Sopov, Eugene Semenkin, Michael Affenzeller

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

4 Zitate (Scopus)

Abstract

In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that applying the mutation operator from differential evolution to the personal best positions of the particles allows for improving the algorithm performance.

OriginalspracheEnglisch
Aufsatznummer154
FachzeitschriftAlgorithms
Jahrgang15
Ausgabenummer5
DOIs
PublikationsstatusVeröffentlicht - Mai 2022

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