Adaptive Differential Whale Optimization Algorithm

Renxing Chen, Yue Hu, Xinyuan Zhang, Jianming Jiang

Keywords: Whale optimization algorithm, Differential Operator, Adaptive inertia weights

Abstract: The whale optimization algorithm (WOA) is effective for solving complex engineering optimization problems, but it often converges slowly and easily gets trapped in local optima. To address these issues, this paper proposes an adaptive differential WOA (ADWOA) with five improvement strategies. A dynamic convergence factor is used to balance exploration and exploitation. Adaptive inertia weights enhance both global and local search. A refined search mechanism enables information exchange across dimensions to improve local accuracy. Laplace-distribution perturbation helps maintain population diversity, and a differential evolution operator is integrated to strengthen search performance. ADWOA is tested on standard benchmark functions, and the results show that it achieves better solution quality and faster convergence than existing methods.

Submission Number: 62