A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm...A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.展开更多
文摘A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.
