Reinforcement learning based parameters adaption method for particle swarm optimization

• URL: http://arxiv.org/abs/2206.00835v1
• Date: Thu, 2 Jun 2022 02:16:15 GMT
• Title: Reinforcement learning based parameters adaption method for particle swarm optimization
• Authors: Yin ShiYuan
• Abstract summary: In this article, a reinforcement learning-based online parameters adaption method(RLAM) is developed to enhance PSO in convergence. experiments on 28 CEC 2013 benchmark functions are carried out when comparing with other online adaption method and PSO variants. The reported results show that the the proposed RLAM is efficient and effictive and that the the proposed RLPSO is more superior compared with several state-of-the-art PSO variants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning-based online parameters adaption method(RLAM) is developed to enhance PSO in convergence by designing a network to control the coefficients of PSO. Moreover, based on RLAM, a new RLPSO is designed. In order to investigate the performance of RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions are carried out when comparing with other online adaption method and PSO variants. The reported computational results show that the proposed RLAM is efficient and effictive and that the the proposed RLPSO is more superior compared with several state-of-the-art PSO variants.

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