TY - JOUR
T1 - A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems
AU - Wang, Xiong
AU - Wei, Yaxin
AU - Guo, Zihao
AU - Wang, Jihong
AU - Yu, Hui
AU - Hu, Bin
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - algorithm enhancement
KW - dung beetle optimization
KW - metaheuristic algorithms
KW - optimization algorithms
KW - sinh and cosh
KW - swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85194044495&partnerID=8YFLogxK
U2 - 10.3390/biomimetics9050271
DO - 10.3390/biomimetics9050271
M3 - Article
AN - SCOPUS:85194044495
SN - 2313-7673
VL - 9
JO - Biomimetics
JF - Biomimetics
IS - 5
M1 - 271
ER -