A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization

Hong, Libin; Yu, Xinmeng; Tao, Guofang; Özcan, Ender; Woodward, John

Abstract

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement.

Citation

Hong, L., Yu, X., Tao, G., Özcan, E., & Woodward, J. (2024). A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization. Complex and Intelligent Systems, 10(2), 2421-2443. https://doi.org/10.1007/s40747-023-01269-z

• Journal Article Type: Article
• Acceptance Date: Oct 12, 2023
• Online Publication Date: Nov 22, 2023
• Publication Date: Apr 1, 2024
• Deposit Date: Dec 15, 2023
• Publicly Available Date: Dec 19, 2023
• Journal: Complex & Intelligent Systems
• Print ISSN: 2199-4536
• Electronic ISSN: 2198-6053
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 2
• Pages: 2421-2443
• DOI: https://doi.org/10.1007/s40747-023-01269-z
• Keywords: Particle swarm optimization, Ratio adaptation scheme, Sequential quadratic programming, Single-objective numerical optimization
• Public URL: https://nottingham-repository.worktribe.com/output/27599511
• Publisher URL: https://link.springer.com/article/10.1007/s40747-023-01269-z