计算机科学
水准点(测量)
启发式
稳健性(进化)
双曲函数
算法
数学
人工智能
化学
大地测量学
生物化学
基因
数学分析
地理
作者
Jianfu Bai,Yifei Li,Mingpo Zheng,Samir Khatir,Brahim Benaissa,Laith Abualigah,Magd Abdel Wahab
标识
DOI:10.1016/j.knosys.2023.111081
摘要
Currently, meta-heuristic algorithms have been widely studied and applied, but balancing exploration and exploitation remains a challenge. In this study, a novel meta-heuristic algorithm named Sinh Cosh Optimizer (SCHO) is proposed based on the mathematical inspiration of the characteristics of Sinh and Cosh. SCHO includes four steps: two different phases of exploration and exploitation, the bounded search strategy, and the switching mechanism. SCHO is compared with eight meta-heuristic algorithms for the 23 benchmark functions at different dimensions and CEC 2014, and its strong performance is validated. The efficiency and robustness of SCHO are verified by qualitative analysis, convergence curves, and two statistical tests. Furthermore, five engineering problems are presented. Source codes of SCHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/130734-a-sinh-cosh-optimizer.
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