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Multi-objective dung beetle optimization algorithm: A novel algorithm for solving complex multi-objective
Wenxing Wu1, Liqin Tian1,2, Junyi Wu1
1School of Computer Science, Qinghai Normal University, Xining, Qinghai, China.
Plos One
|October 14, 2025
Summary
This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO) for complex problems. MODBO enhances search capabilities with competitive and neighborhood mechanisms, demonstrating effectiveness in real-world applications.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Swarm Intelligence
Background:
- Increasing complexity of multi-objective optimization problems (MOPs) necessitates advanced algorithms.
- Existing algorithms may struggle with convergence and maintaining search diversity in complex MOPs.
Purpose of the Study:
- Introduce the Multi-Objective Dung Beetle Optimization Algorithm (MODBO) to address challenges in MOPs.
- Enhance the optimization process through novel competitive and neighborhood mechanisms.
Main Methods:
- Adapted the Dung Beetle Optimization Algorithm using non-dominated sorting for MOPs.
- Integrated a Competition mechanism for global search and a Neighborhood mechanism for local search.
- Utilized an external archive for maintaining generational optimality.
Main Results:
- MODBO demonstrated competitive performance against nine other algorithms on the CEC2020 benchmark.
- Successfully applied MODBO to the 3D sensor deployment problem, showcasing its real-world applicability.
- The integrated mechanisms improved global and local search capabilities.
Conclusions:
- MODBO is an effective algorithm for solving complex multi-objective optimization problems.
- The proposed enhancements significantly improve the performance and robustness of the Dung Beetle Optimization Algorithm.
- MODBO shows promise for addressing practical optimization challenges.
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