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Huo, J., Liu, L. and Zhang, Y. (2018) An Improved Multi-Cores Parallel Artificial Bee Colony Optimization Algorithm for Parameters Calibration of Hydrological Model. Future Generation Computer Systems, 81, 492-504.
https://doi.org/10.1016/j.future.2017.07.020

has been cited by the following article:

• TITLE: Arctic Puffin Optimization Algorithm Based on Multi-Strategy Blending

  AUTHORS: Ling Sun, Bo Wang

  KEYWORDS: Arctic Puffin Optimization, Elite Reverse Learning Strategy, Tangential Flight Strategy, Adaptive t-Distribution Variation Strategy

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.12, December 30, 2024

  ABSTRACT: 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.