Multi-Strategy Fusion Improved Dung Beetle Optimization Algorithm and Engineering Design Application
Daming Zhang, Zijian Wang, Yanqing Zhao, Fangjin Sun
Abstract
In this paper, a Multi-Strategy fusion Improved Dung Beetle Optimization (MSIDBO) algorithm is proposed to address the problems that the dung beetle optimization algorithm is prone to fall into local optimum, suffers from weak global exploration, and shows slow convergence to a solution. In MSIDBO, a good point set strategy is adopted to generate a more diverse initial population; Golden sine and t-distribution perturbation strategies are used to improve the global search capability of the algorithm; By introducing the adaptive Gaussian-Cauchy mutation strategy, the probability of the algorithm falling into local optimal is reduced. To verify the effectiveness of the MSIDBO algorithm, it was tested against 23 benchmark functions and 29 CEC-2017 test functions, comparing its performance with that of other well-known metaheuristic algorithms. The results show that the MSIDBO algorithm excelled in 17 out of 23 benchmark test functions, achieving higher solution accuracy and faster convergence compared to the original DBO algorithm, while the remaining 6 functions yielded comparable results. Similarly, among the 29 CEC-2017 test functions, the MSIDBO algorithm surpassed the original DBO algorithm in 25, and the remaining 4 functions yielded similar results. Additionally, to verify the practical application potential of the MSIDBO algorithm, this paper applies it to optimize three engineering design problems, and the experimental results show that the MSIDBO algorithm has a higher application potential compared with other algorithms.