Reinforcement learning based parameters adaption method for particle swarm optimization

TL;DR

This paper introduces a reinforcement learning-based online parameter adaptation method for particle swarm optimization, significantly improving convergence speed and outperforming existing PSO variants on benchmark functions.

Contribution

The paper proposes a novel RL-based online parameter adaptation method (RLAM) and a new RLPSO algorithm, enhancing PSO's convergence and performance.

Findings

RLAM is efficient and effective.

RLPSO outperforms several state-of-the-art PSO variants.

Experimental results on benchmark functions validate the approach.

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.