A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

被引:27
|
作者
Su, Hang [1 ]
Zhao, Dong [1 ]
Yu, Fanhua [1 ]
Heidari, Ali Asghar [2 ]
Xu, Zhangze [2 ]
Alotaibi, Fahd S. [3 ]
Mafarja, Majdi [3 ,4 ]
Chen, Huiling [2 ]
机构
[1] Changchun Normal Univ, Coll Comp Sci & Technol, Changchun 130032, Jilin, Peoples R China
[2] Wenzhou Univ, Coll Comp Sci & Artificial Intelligence, Wenzhou 325035, Zhejiang, Peoples R China
[3] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia
[4] Birzeit Univ, Dept Comp Sci, POB 14, Birzeit, Palestine
基金
中国国家自然科学基金;
关键词
swarm intelligence; metaheuristic; engineering design; cuckoo search algorithm; crisscross optimizer; disperse foraging strategy; OBJECTIVE DEPLOYMENT OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; DIFFERENTIAL EVOLUTION; COMPUTATIONAL INTELLIGENCE; RESOURCE-ALLOCATION; SOLVING SYSTEMS; ALGORITHM; DESIGN; EFFICIENT;
D O I
10.1093/jcde/qwac112
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.
引用
收藏
页码:36 / 64
页数:29
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