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The Dung beetle optimizer (DBO) is a novel metaheuristic algorithm with certain limitations, including suboptimal convergence accuracy and local optima entrapment. To address these issues, this study proposes a modified reinforced DBO (MRDBO) featuring three key enhancements: (1) performing chaotic mapping for improved population initialization, (2) RIME-inspired soft-rime search for faster convergence, and (3) adaptive hybrid mutation with greedy selection to prevent stagnation. A comprehensive evaluation using 14 benchmark functions against 30 competing algorithms confirms the superior performance of MRDBO. Practical validation through assembly line balancing applications further demonstrated its effectiveness.

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Multistrategy Improved Dung Beetle Optimization Algorithm and Its Application in ALBP-1

  • 会议论文

  • 发表日期：

    2026年

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  • Bing Wan,
  • Haibo Cui,
  • Qin Guo,
  • Lingyan Zhang

摘要

The Dung beetle optimizer (DBO) is a novel metaheuristic algorithm with certain limitations, including suboptimal convergence accuracy and local optima entrapment. To address these issues, this study proposes a modified reinforced DBO (MRDBO) featuring three key enhancements: (1) performing chaotic mapping for improved population initialization, (2) RIME-inspired soft-rime search for faster convergence, and (3) adaptive hybrid mutation with greedy selection to prevent stagnation. A comprehensive evaluation using 14 benchmark functions against 30 competing algorithms confirms the superior performance of MRDBO. Practical validation through assembly line balancing applications further demonstrated its effectiveness.