Ye Mingjun, Zhou Heng, Yang Haoyu, Hu Bin, Wang Xiong
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Department of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi 214028, China.
Biomimetics (Basel). 2024 May 13;9(5):291. doi: 10.3390/biomimetics9050291.
The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.
蜣螂优化（DBO）算法是一种基于群体智能的元启发式算法，以其强大的优化能力和快速的收敛速度而闻名。然而，它也存在种群多样性低、易陷入局部最优解以及在面对复杂优化问题时收敛速度不理想等问题。针对这些问题，本文提出了多策略改进蜣螂优化算法（MDBO）。核心改进包括使用拉丁超立方采样进行更好的种群初始化，以及引入一种名为“均值差分变异”的新型差分变异策略，以增强算法规避局部最优的能力。此外，还提出了一种结合透镜成像反向学习和逐维优化的策略，并将其应用于当前最优解。通过对CEC2017和CEC2020的标准基准函数进行全面性能测试，与其他经典元启发式优化算法相比，MDBO在优化精度、稳定性和收敛速度方面表现出卓越的性能。此外，通过扩展/压缩弹簧设计问题、减速器设计问题和焊接梁设计问题这三个具有代表性的工程应用场景，验证了MDBO在解决复杂实际工程问题方面的有效性。
