2024 | Qinghua Li *, Hu Shi, Wanting Zhao and Chunlu Ma
The paper introduces an enhanced dung beetle optimization algorithm (EDBO) designed to solve nonlinear optimization problems with multiple constraints in manufacturing. The EDBO improves upon the original dung beetle optimization (DBO) algorithm by addressing its limitations, such as insufficient global search capabilities and poor convergence precision. The enhancements include:
1. **Rolling Phase Improvement**: The worst value interference is removed, and the current solution is coupled with the optimal solution to retain the advantages of the original formulation.
2. **Dancing Phase Improvement**: The global optimal solution is introduced to guide the dancing phase, and a stochastic factor is added to the optimal solution to enhance exploration.
3. **Foraging Phase Improvement**: The Jacobi curve is introduced to improve the algorithm's ability to escape local optima and avoid premature convergence.
The performance of EDBO is evaluated using the CEC2017 function set, and its effectiveness is verified through Wilcoxon rank-sum test and Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and stability. Additionally, EDBO is applied to solve four real engineering optimization problems, demonstrating its adaptability and robustness.
The paper concludes that EDBO is an efficient algorithm for solving complex engineering optimization problems, with good stability and practicality. Future work will focus on improving EDBO for multi-objective optimization and applying it to more complex engineering applications.The paper introduces an enhanced dung beetle optimization algorithm (EDBO) designed to solve nonlinear optimization problems with multiple constraints in manufacturing. The EDBO improves upon the original dung beetle optimization (DBO) algorithm by addressing its limitations, such as insufficient global search capabilities and poor convergence precision. The enhancements include:
1. **Rolling Phase Improvement**: The worst value interference is removed, and the current solution is coupled with the optimal solution to retain the advantages of the original formulation.
2. **Dancing Phase Improvement**: The global optimal solution is introduced to guide the dancing phase, and a stochastic factor is added to the optimal solution to enhance exploration.
3. **Foraging Phase Improvement**: The Jacobi curve is introduced to improve the algorithm's ability to escape local optima and avoid premature convergence.
The performance of EDBO is evaluated using the CEC2017 function set, and its effectiveness is verified through Wilcoxon rank-sum test and Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and stability. Additionally, EDBO is applied to solve four real engineering optimization problems, demonstrating its adaptability and robustness.
The paper concludes that EDBO is an efficient algorithm for solving complex engineering optimization problems, with good stability and practicality. Future work will focus on improving EDBO for multi-objective optimization and applying it to more complex engineering applications.