Chaotic Sparrow Search Optimizer for Structural Design Challenges

A traditional swarm intelligence method called the Sparrow Search Optimizer (SSO), which was motivated by the natural behaviours of sparrows, has demonstrated potential in optimization tasks. We suggest a unique modification that integrates the chaotic Singer map into the SSO framework to improve its functionality. This results in the Chaotic Sparrow Search Optimizer (CSSO). The goal of this study is to use chaos to create better structural designs. This study presents the implementation details for CSSO and conducts a thorough analysis of its effectiveness in comparison to the traditional SSO and other metaheuristics. The suggested approach is used to optimize complicated structures with various design restrictions and aims. Simulation tests show that in terms of solution quality and convergence speed, CSSO surpasses the traditional SSO and other metaheuristics. By including the chaotic Singer map, CSSO is better able to explore and exploit challenging problem areas and consistently provide better ideas. CSSO exhibits potential as a useful tool for structural engineering designs, providing a cutting-edge method to address engineeirng design issues.