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

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

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)2421-2443
Number of pages23
JournalComplex and Intelligent Systems
Volume10
Issue number2
DOIs
Publication statusPublished - Apr 2024
Externally publishedYes

Keywords

  • Particle swarm optimization
  • Ratio adaptation scheme
  • Sequential quadratic programming
  • Single-objective numerical optimization

ASJC Scopus subject areas

  • Information Systems
  • Engineering (miscellaneous)
  • Computational Mathematics
  • Artificial Intelligence

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