Article: A many-objective particle swarm optimisation algorithm based on convergence assistant strategy Journal: International Journal of Bio-Inspired Computation (IJBIC) 2022 Vol.20 No.2 pp.104 - 118 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. Inderscience Publishers - linking academia, business and industry through research

Title: A many-objective particle swarm optimisation algorithm based on convergence assistant strategy

Authors: Wusi Yang; Li Chen; Yanyan Li; Fazeel Abid

Addresses: School of Computer Science, Xianyang Normal University, Xianyang, China; School of Information Technology and Software, Northwest University, Xi'an, China ' School of Information Technology and Software, Northwest University, Xi'an, China ' School of Science, Xi'an Technology University, Xi'an, China ' Department of Information System, School of Business and Economics, University of Management and Technology, Lahore, Pakistan

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.

Keywords: many-objective optimisation; particle swarm optimisation; PSO; self-controlling dominance area of solutions; S-CDAS; convergence assistant strategy.

DOI: 10.1504/IJBIC.2022.126773

International Journal of Bio-Inspired Computation, 2022 Vol.20 No.2, pp.104 - 118

Received: 01 Jun 2021
Accepted: 19 Oct 2021
Published online: 07 Nov 2022 *

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Title: A many-objective particle swarm optimisation algorithm based on convergence assistant strategy

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