An adaptive chaotic league championship algorithm for solving global optimization and engineering design problems

• Development of a novel metaheuristic called AC-LCA. • The guide selection approach of the original LCA and CLCA is re-designed with the adaptive chaotic operator. • Standard benchmark and CEC2017 suite are considered for performance validation. • The practicability of AC-LCA is validated on global and engineering design problems. • A wide range of evaluation methods are applied to show the supremacy of the AC-LCA. This paper introduces a novel approach to global numerical optimization through the development of an Adaptive Chaotic League Championship Algorithm (AC-LCA). Our methodology enhances the conventional League Championship Algorithm (LCA) by integrating an adaptive chaotic local search mechanism. This integration aims to improve the exploration and exploitation capabilities of the LCA, enabling it to effectively navigate complex search spaces and avoid premature convergence. Abundant experiments have been extensively executed on the well-known CEC2017 benchmark problem sets to validate the performance of AC-LCA. The results demonstrate significant improvements in convergence speed and solution accuracy over traditional LCA and several other state-of-the-art optimization algorithms. Notably, the adaptive chaotic component plays a critical role in fine-tuning the search process, contributing to the robustness and efficiency of the algorithm. The paper also investigates the application of AC-LCA to a set of five famous real-life engineering problems, showcasing its practicality and adaptability in diverse optimization scenarios. These applications further underline the algorithm's potential to address a wide range of complex optimization tasks, making it a valuable tool for researchers and practitioners in the field. Overall, the AC-LCA emerges as a promising new approach in global numerical optimization, offering a balance of innovative methodology and practical applicability.