An adaptive oppositional grey wolf optimizer for complex engineering problems
Abstract
Metaheuristic optimization algorithms are critical for solving complex, high-dimensional problems, however, their performance is frequently hindered by premature convergence, limited exploration capabilities, and an imbalance between search phases. While the grey wolf optimizer (GWO) has demonstrated considerable potential, its rigid linear control parameters and susceptibility to elite stagnation limit its scalability in highly deceptive landscapes. To overcome these critical drawbacks, this paper proposes the adaptive oppositional grey wolf optimizer (AOGWO), a robust and dynamically adaptive optimization framework. The proposed approach makes three primary contributions: (1) integrating a hybrid opposition-based learning (OBL) initialization framework to guarantee maximum initial spatial diversity, (2) implementing an adaptive cosine control strategy paired with a decaying Jumping Rate and Lévy flight perturbations to dynamically balance exploration and exploitation, and (3) proposing a highly targeted selective leading opposition (SLO) mechanism applied exclusively to the Alpha leader to prevent elite traps without incurring excessive computational overhead. The performance of AOGWO is evaluated using an extensive suite of 41 benchmark test functions (comprising the IEEE CEC2017 and CEC2022 suites) and seven challenging real-world engineering design problems. Comprehensive empirical results provide concrete data demonstrating AOGWO's competitive performance.…
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