Ensemble strategy using particle swarm optimisation variant and enhanced local search capability

Hong, Libin; Wang, Guodong; Özcan, Ender; Woodward, John

Abstract

Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing suitable mechanisms at appropriate instances in the evolutionary process. This study entailed the development of an ensemble strategy that uses a variant of the modified particle swarm optimisation algorithm with a covariance matrix adapted to the retreat phase and sequential quadratic programming. The modified particle swarm optimisation algorithm employs nonlinear population size reduction and uses the candidate elite best solution as the stochastic learning strategy and the fitness-distance balance as the terminal updating mechanism. The proposed algorithm was compared with the most recently proposed particle swarm optimisation-based variants through testing on CEC2017 benchmark functions. In the experimental results, the proposed method achieved the best ranking and exhibited excellent performance. Further effectiveness tests demonstrated that the proposed combination of algorithms and mechanisms exhibits tacit cooperation and significantly improves performance.

Citation

Hong, L., Wang, G., Özcan, E., & Woodward, J. (2023). Ensemble strategy using particle swarm optimisation variant and enhanced local search capability. Swarm and Evolutionary Computation, 84, Article 101452. https://doi.org/10.1016/j.swevo.2023.101452

• Journal Article Type: Article
• Acceptance Date: Dec 10, 2023
• Online Publication Date: Dec 11, 2023
• Publication Date: Dec 11, 2023
• Deposit Date: Dec 18, 2023
• Publicly Available Date: Dec 12, 2024
• Journal: Swarm and Evolutionary Computation
• Print ISSN: 2210-6502
• Electronic ISSN: 2210-6510
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 84
• Article Number: 101452
• DOI: https://doi.org/10.1016/j.swevo.2023.101452
• Keywords: Ensemble strategy, Particle swarm optimisation, Covariance matrix adapted retreat, Sequential quadratic programming, Fitness-distance balance
• Public URL: https://nottingham-repository.worktribe.com/output/28434021
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2210650223002249?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Ensemble strategy using particle swarm optimisation variant and enhanced local search capability; Journal Title: Swarm and Evolutionary Computation; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.swevo.2023.101452