Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The dung beetle optimization (DBO) algorithm is an efficient swarm intelligence technique known for its rapid convergence.
• A common limitation of DBO and similar algorithms is entrapment in local optima during later optimization stages.
• Addressing this limitation is crucial for improving the reliability and effectiveness of swarm intelligence methods.

Purpose of the Study:

• To propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm to overcome local optima stagnation in DBO.
• To enhance the global exploration and local exploitation balance within the optimization process.
• To validate the efficacy of MEDBO on benchmark functions and real-world engineering problems.

Main Methods:

• MEDBO employs a good point set strategy for uniform initial population distribution, reducing initial local optima risk.
• The algorithm integrates convergence factors to guide the search process effectively.
• MEDBO dynamically balances offspring and foraging individuals, prioritizing global exploration then local refinement.

Main Results:

• MEDBO demonstrated significant performance improvements on the CEC2017 benchmark suite compared to existing methods.
• The algorithm successfully applied to engineering design problems, including pressure vessel, three-bar truss, and spring design.
• MEDBO effectively mitigated local optima stagnation, showcasing enhanced search capabilities.

Conclusions:

• MEDBO offers a robust solution to the local optima problem inherent in swarm intelligence algorithms.
• The proposed algorithm exhibits practical efficacy and superior performance in complex engineering design tasks.
• MEDBO represents a valuable advancement in optimization techniques for scientific and engineering applications.