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

Research output: Contribution to journalArticlepeer-review

5 Citations (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.

Original languageEnglish
Article number154
JournalAlgorithms
Volume15
Issue number5
DOIs
Publication statusPublished - May 2022

Keywords

  • differential evolution
  • dynamic environments
  • evolutionary algorithms
  • particle swarm optimization

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