A sophisticated solution to numerical and engineering optimization problems using Chaotic Beluga Whale Optimizer

Beluga Whale Optimization (BWO) metaheuristic search algorithm has recently emerged as a promising approach to address benchmark optimization problems. However, the local search phase of the fundamental BWO algorithm has been observed to suffer from low rate of convergence, stemming from its inadequate exploitation capabilities. The aim of this study is to present a hybrid algorithm, called Chaotic Beluga Whale Optimization (CBWO), to bolster the potential of this technique. CBWO combines chaotic behavior to reach a balance between exploration and exploitation, aiming for improved performance. To assess the effectiveness of CBWO, comprehensive evaluation is conducted on 23 common benchmark functions, and a comparative comparison is performed with several existing algorithms to showcase the advantages of the proposed approach. Furthermore, to ascertain its practical utility, CBWO is applied to 11 traditional engineering challenges and the results are compared with other state-of-the-art algorithms. The findings of these studies show that CBWO demonstrates greater efficiency in optimization, demonstrating quicker and more accurate convergence rates. Specifically, CBWO achieves an average convergence rate improvement of 23% over BWO and outperforms other algorithms by up to 14.8% in terms of solution accuracy. Pseudocode for the CBWO algorithm, enabling easy implementation and understanding, is also presented. Results of this study emphasize the potential of CBWO as a promising optimization tool for addressing complex real-world problems effectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.