Adaptive parameter tuning of chaotic search using particle swarm optimization

Abstract

Chaotic search has demonstrated promising performance for solving combinatorial optimization. However, its effectiveness is highly sensitive to parameter settings of chaotic neural networks, and empirical tuning often fails to maintain stable performance across different problems and searching stages. To address this issue, we propose a chaotic search method with particle swarm optimization, a learning-based adaptive tuning method that integrates particle swarm optimization into the chaotic search framework. Within this framework, particle swarm optimization serves as an online learning mechanism that dynamically optimizes key parameters, enabling the automatic regulation of neural excitation during the search process. Comparative experiments on capacitated vehicle routing problems reveal that the chaotic search method with particle swarm optimization has better solution quality and higher robustness compared with conventional chaotic search and feedback-based tuning methods. These results confirm the efficacy of swarm-based online parameter learning for enhancing search performance of chaotic search.