# Reinforcement learning based parameters adaption method for particle swarm optimization

arXiv: https://arxiv.org/abs/2206.00835 · HTML: https://mlsanity.com/paper/2206.00835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2022.

## Built on by (most cited first, 4 of 4)

- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA](https://mlsanity.com/paper/2606.00862/) (2026)
- [Reinforcement Learning Based Sensor Optimization for Bio-markers](https://mlsanity.com/paper/2308.10649/) (2023)
- [A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs](https://mlsanity.com/paper/2506.04238/) (2025)

## Abstract (arXiv)

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.