Hybrid Algorithm of Improved Beetle Antenna Search and Artificial Fish Swarm

被引:4
|
作者
Ni, Jian [1 ]
Tang, Jing [1 ]
Wang, Rui [1 ]
机构
[1] Hebei Univ Engn, Sch Informat & Elect Engn, Handan 056038, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 24期
关键词
artificial fish swarm algorithm; beetle antenna search algorithm; hybrid algorithm; mutation strategy; multistep detection;
D O I
10.3390/app122413044
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The beetle antenna search algorithm (BAS) converges rapidly and runs in a short time, but it is prone to yielding values corresponding to local extrema when dealing with high-dimensional problems, and its optimization result is unstable. The artificial fish swarm algorithm (AFS) can achieve good convergence in the early stage, but it suffers from slow convergence speed and low optimization accuracy in the later stage. Therefore, this paper combines the two algorithms according to their respective characteristics and proposes a mutation and a multi-step detection strategy to improve the BAS algorithm and raise its optimization accuracy. To verify the performance of the hybrid composed of the AFS and BAS algorithms based on the Mutation and Multi-step detection Strategy (MMSBAS), AFS-MMSBAS is compared with AFS, the Multi-direction Detection Beetle Antenna Search (MDBAS) Algorithm, and the hybrid algorithm composed of the two (AFS-MDBAS). The experimental results show that, with respect to high-dimensional problems: (1) the AFS-MMSBAS algorithm is not only more stable than the MDBAS algorithm, but it is also faster in terms of convergence and operation than the AFS algorithm, and (2) it has a higher optimization capacity than the two algorithms and their hybrid algorithm.
引用
收藏
页数:17
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