A novel particle swarm optimization based on hybrid-learning model

Yufeng Wang; BoCheng Wang; Zhuang Li; Chunyu Xu; Yufeng Wang; BoCheng Wang; Zhuang Li; Chunyu Xu

doi:10.3934/mbe.2023305

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7056-7087. doi: 10.3934/mbe.2023305

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A novel particle swarm optimization based on hybrid-learning model

1.
School of Computer Science and Software, Nanyang Institute of Technology, Nanyang 473306, China
2.
Electronic Information School, Wuhan University, Wuhan 430072, China
3.
School of Information Engineering, Nanyang Institute of Technology, Nanyang 473306, China

Academic Editor: Guenther Palm

Received: 10 December 2022 Revised: 11 January 2023 Accepted: 16 January 2023 Published: 09 February 2023

The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms.
- particle swarm optimization,
- adaptive parameters,
- multi-pool fusion,
- hybrid-learning model,
- Gaussian Perturbation
Citation: Yufeng Wang, BoCheng Wang, Zhuang Li, Chunyu Xu. A novel particle swarm optimization based on hybrid-learning model[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7056-7087. doi: 10.3934/mbe.2023305

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Abstract

The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms.

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