Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Mingjun Ye , Heng Zhou, Haoyu Yang, Bin Hu, Xiong Wang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed “Mean Differential Variation”, to enhance the algorithm’s ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

Original languageEnglish
Article number291
JournalBiomimetics
Volume9
Issue number5
DOIs
StatePublished - May 2024

Keywords

  • Latin hypercube sampling
  • dimension-by-dimension optimization
  • dung beetle optimization algorithm
  • mean differential variation

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