Comparative Analysis and Evaluation: A Case Study for Multiple Chaotic Mapping Snake Optimizer

Abstract In recent years, chaos theory has been widely incorporated into metaheuristic optimization algorithms to enhance their search performance. However, for a given algorithm, there is still a lack of systematic and large-scale comparison regarding the effectiveness of different chaotic maps, and no clear consensus has yet been reached. To address this issue, this paper reviews twelve commonly used classical chaotic maps and summarizes three typical integration strategies of chaotic maps in bio-inspired optimization algorithms. Taking the recently proposed Snake Optimizer, a representative bio-inspired algorithm with distinct exploration and exploitation phases, as a case study, comprehensive simulation experiments are conducted to investigate the performance impact of different chaotic maps. The proposed chaotic-map-based variants are evaluated on the CEC 2022 benchmark test functions as well as several real-world engineering design problems. The experimental results indicate that the performance improvement brought by chaotic maps is highly dependent on the map type and problem characteristics. Overall, the Tent chaotic map demonstrates the most consistent and significant enhancement among the considered variants, while other maps exhibit varying levels of effectiveness. In addition, a comparative analysis of the chaotic Snake Optimizer is presented, highlighting its advantages, limitations, and potential directions for future research.