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Improving the Dung Beetle Optimizer with Multiple Strategies: An Application to Complex Engineering Problems.

Wei Lv1, Yueshun He1,2, Yuankun Yang1

  • 1School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China.

Biomimetics (Basel, Switzerland)
|November 26, 2025
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Summary

The Multi-Strategy Improved Dung Beetle Optimizer (MIDBO) enhances global optimization by addressing premature convergence. This novel algorithm demonstrates superior performance on complex challenges and engineering problems.

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DBOengineering optimization problemsimproved dung beetle foraging strategymulti-population differential co-evolutionary mechanismoscillating balance factor

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The standard Dung Beetle Optimizer (DBO) shows promise but suffers from premature convergence and precision issues in complex optimization.
  • Existing metaheuristics often require improvements for enhanced performance on challenging problems.

Purpose of the Study:

  • To develop an advanced optimization algorithm, the Multi-Strategy Improved Dung Beetle Optimizer (MIDBO), to overcome the limitations of the standard DBO.
  • To enhance the global search capability, local refinement precision, and convergence speed of the DBO algorithm.

Main Methods:

  • Incorporated Circle chaotic map with dynamic opposition-based learning for improved initial population diversity.
  • Introduced a nonlinear oscillating balance factor and enhanced foraging strategy for dynamic equilibrium between global and local search.
  • Implemented a multi-population differential co-evolutionary mechanism with fitness-based partitioning and unique mutation operators to avoid local optima.

Main Results:

  • MIDBO demonstrated significantly superior optimization performance compared to other metaheuristics on CEC2017 and CEC2022 benchmark functions.
  • The algorithm's effectiveness was validated through successful application to three practical engineering challenges.
  • Comparative studies confirmed MIDBO's enhanced convergence speed and ability to avoid local optima.

Conclusions:

  • The Multi-Strategy Improved Dung Beetle Optimizer (MIDBO) effectively addresses the limitations of the standard DBO, offering improved global optimization capabilities.
  • MIDBO's novel strategies result in superior performance and practical applicability for complex optimization tasks and engineering problems.
  • The proposed algorithm represents a significant advancement in metaheuristic optimization techniques.