A many-objective particle swarm optimisation algorithm based on convergence assistant strategy. (1st November 2022)

• Record Type: Journal Article
• Authors: Yang, Wusi; Chen, Li; Li, Yanyan; Abid, Fazeel
• Is Part Of: International journal of bio-inspired computation. Volume 20:Number 2(2022)
• Page Start: 104
• Page End: 118
• Publication Date: 2022-11-01
• Subjects: many-objective optimisation -- particle swarm optimisation -- PSO -- self-controlling dominance area of solutions -- S-CDAS -- convergence assistant strategy; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

The multi-objective particle swarm optimisation algorithm based on Pareto dominance also has specific dilemmas when dealing with many-objective optimisation problems. For example, how to make the algorithm more effectively approach the true Pareto front, and maintain the diversity of solutions. This paper proposes a convergence assistance framework that couples different convergence operators separately to handle many-objective optimisation problems. To maintain the convergence and diversity of populations in the environmental selection, random sampling was performed on the population obtained by the shift density estimation and vector angle. The proposed algorithm is compared with several advanced many-objective optimisation algorithms on test suites DTLZ and MaF with 4, 6, 8, 10 and 15 objectives. The experimental results show that the proposed algorithm has better convergence and diversity, outperforms most of the comparison algorithms, and verifies the robustness of the algorithm framework.