Multi-objective particle swarm optimization with reverse multi-leaders

Fei Chen; Yanmin Liu; Jie Yang; Meilan Yang; Qian Zhang; Jun Liu; Fei Chen; Yanmin Liu; Jie Yang; Meilan Yang; Qian Zhang; Jun Liu

doi:10.3934/mbe.2023522

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 11732-11762. doi: 10.3934/mbe.2023522

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Research article

Multi-objective particle swarm optimization with reverse multi-leaders

1.
School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China
2.
School of Mathematics, Zunyi Normal College, Zunyi 563002, China
3.
School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China

Academic Editor: Yang Kuang

Received: 09 February 2023 Revised: 09 April 2023 Accepted: 23 April 2023 Published: 09 May 2023

Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversity of solutions in the archives. Second, a reverse selection method is proposed to select two global leaders for the particles in the population. This is conducive to selecting appropriate learning samples for each particle and leading the particles to quickly fly to the true Pareto front. Third, an information fusion strategy is proposed to update the personal best, to improve convergence of the algorithm. At the same time, in order to achieve a better balance between convergence and diversity, a new particle velocity updating method is proposed. With this, two global leaders cooperate to guide the flight of particles in the population, which is conducive to promoting the exchange of social information. Finally, RMMOPSO is simulated with several state-of-the-art MOPSOs and multi-objective evolutionary algorithms (MOEAs) on 22 benchmark problems. The experimental results show that RMMOPSO has better comprehensive performance.
- multi-objective particle swarm optimization,
- global ranking,
- mean angular distance,
- reverse selection,
- information fusion
Citation: Fei Chen, Yanmin Liu, Jie Yang, Meilan Yang, Qian Zhang, Jun Liu. Multi-objective particle swarm optimization with reverse multi-leaders[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 11732-11762. doi: 10.3934/mbe.2023522

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Abstract

Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversity of solutions in the archives. Second, a reverse selection method is proposed to select two global leaders for the particles in the population. This is conducive to selecting appropriate learning samples for each particle and leading the particles to quickly fly to the true Pareto front. Third, an information fusion strategy is proposed to update the personal best, to improve convergence of the algorithm. At the same time, in order to achieve a better balance between convergence and diversity, a new particle velocity updating method is proposed. With this, two global leaders cooperate to guide the flight of particles in the population, which is conducive to promoting the exchange of social information. Finally, RMMOPSO is simulated with several state-of-the-art MOPSOs and multi-objective evolutionary algorithms (MOEAs) on 22 benchmark problems. The experimental results show that RMMOPSO has better comprehensive performance.

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