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In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms.

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A Dimensional Difference-Based Population Size Adjustment Framework for Gannet Optimization Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Jeng-Shyang Pan,
  • Kunpeng Han,
  • Shu-Chuan Chu,
  • Zhi Li,
  • Li Zhang

摘要

In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms.