Multi-strategy Integrated Grey Wolf Optimization Algorithm

Abstract The grey wolf optimizer (GWO) has gained significant recognition in metaheuristic research owing to its straightforward implementation and minimal parameter configuration requirements. Nevertheless, conventional GWO implementations exhibit limitations in addressing complex optimization challenges, particularly premature convergence tendencies and susceptibility to local optima entrapment. This investigation presents an enhanced multi-strategy framework incorporating chaotic initialization sequences, dual nonlinear convergence operators, adaptive weighting mechanisms, and elite preservation techniques, while synergistically integrating predation strategies from both Aquila Optimizer (AO) and Whale Optimization Algorithm (WOA). The methodological innovations unfold through three primary phases: (1) Chaotic logistic mapping generates diversified initial populations to prevent solution clustering; (2) An arctangent-based convergence operator dynamically regulates leadership parameters across exploration-exploitation phases; (3) Hybridized search patterns combine AO's vertical dive dynamics with WOA's spiral updating mechanisms. Population diversity maintenance is achieved through elite archival strategies that preserve superior solutions throughout iterations. Rigorous evaluation employing CEC2005's 23 benchmark functions reveals substantial improvements in convergence precision and computational efficiency compared with eight contemporary optimization algorithms. The algorithm is used for three real-world engineering problems, and the results show that the proposed algorithm is very effective in the unknown exploration space for challenging problems.