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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.

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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.