Chaos-enhanced white shark optimization algorithms CWSO for global optimization

Abstract

Metaheuristic optimization algorithms are vital across various domains but often struggle with convergence to local optima, limiting their potential to discover globally optimal solutions. Integrating chaotic maps into the optimization process has proven particularly advantageous, as it broadens search capabilities, accelerates convergence, and reduces the likelihood of getting trapped in local minima. We present an optimized algorithm, the Chaotic White Shark Optimizer (CWSO), which incorporates ten different chaotic maps to replace random sequences in key components of the standard White Shark Optimizer (WSO). This modification aims to effectively balance the exploration and exploitation phases, thereby enhancing the probability of finding globally optimal solutions. The CWSO was evaluated on 23 benchmark functions and applied to engineering problems, demonstrating its robustness and reliability. Furthermore, it was used for reconstructing signals and 2D/3D medical images. Comparative evaluations with six well-known metaheuristic algorithms showed that the CWSO outperformed the original WSO and other existing algorithms, offering superior performance in terms of solution quality, global optimality, and avoiding local minima.