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

Autor: Libin Hong, Xinmeng Yu, Guofang Tao, Ender Özcan, John Woodward
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 10, Iss 2, Pp 2421-2443 (2023)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-023-01269-z
Popis: 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.
Databáze: Directory of Open Access Journals
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