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<p>This paper proposes an improved version of the Honey Badger Algorithm (HBA) for multi-objective optimization problems, referred to as the Improved Multi-Objective Honey Badger Algorithm (IMOHBA). The collective behavior search strategy of the HBA is integrated with a dynamic archive to efficiently retrieve and store Pareto optimal solutions. Additionally, a leader selection mechanism based on crowding distance and the roulette wheel strategy is employed to select the optimal solution in multi-objective space. To overcome the issue of local optima, a modified mutualism phase from the Symbiotic Organisms Search (SOS) algorithm is introduced to enhance the global search capability. The algorithm is tested on CEC2009 benchmark functions and various real-world engineering problems, demonstrating competitive performance for solving complex multi-objective optimization problems.</p>

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An improved multi-objective honey badger algorithm based on global searching strategy

  • 期刊论文

  • 发表日期：

    2025年4月3日

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  • Jiarui Cui,
  • Hao Zhou,
  • Qun Yan,
  • Jian Huang,
  • Minggang Wang,
  • Xu Yang,
  • Qing Li

摘要

This paper proposes an improved version of the Honey Badger Algorithm (HBA) for multi-objective optimization problems, referred to as the Improved Multi-Objective Honey Badger Algorithm (IMOHBA). The collective behavior search strategy of the HBA is integrated with a dynamic archive to efficiently retrieve and store Pareto optimal solutions. Additionally, a leader selection mechanism based on crowding distance and the roulette wheel strategy is employed to select the optimal solution in multi-objective space. To overcome the issue of local optima, a modified mutualism phase from the Symbiotic Organisms Search (SOS) algorithm is introduced to enhance the global search capability. The algorithm is tested on CEC2009 benchmark functions and various real-world engineering problems, demonstrating competitive performance for solving complex multi-objective optimization problems.