A modified crow search algorithm based on group strategy and adaptive mechanism

被引:4
作者
Liu, Zhao [1 ]
Wang, Wenjie [2 ,3 ]
Shi, Guohong [4 ]
Zhu, Ping [2 ,3 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Design, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ, Natl Engn Res Ctr Automot Power & Intelligent Cont, Sch Mech Engn, Shanghai, Peoples R China
[4] Pan AsiaTechn Automot Ctr Co Ltd, Shanghai, Peoples R China
基金
中国国家自然科学基金; 上海市自然科学基金;
关键词
Metaheuristic algorithm; crow search algorithm; group strategy; adaptive mechanism; engineering design problems; PARTICLE SWARM OPTIMIZATION; SYMBIOTIC ORGANISMS SEARCH; DIFFERENTIAL EVOLUTION; DESIGN;
D O I
10.1080/0305215X.2023.2173747
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms.
引用
收藏
页码:625 / 643
页数:19
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