数学优化
粪甲虫
元启发式
初始化
计算机科学
人口
趋同(经济学)
算法
最优化问题
数学
生态学
金龟子科
人口学
社会学
经济
生物
程序设计语言
经济增长
作者
Wei Xiong,Wei Yao,Zhiheng Guo,Jihong Wang,Hui Yang Yu,Bin Hu
出处
期刊:Biomimetics
[MDPI AG]
日期:2024-04-29
卷期号:9 (5): 271-271
被引量:1
标识
DOI:10.3390/biomimetics9050271
摘要
The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.
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