Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

被引:2
|
作者
Liu, Haijun [1 ]
Zhou, Rui [1 ]
Zhong, Xiaoyong [1 ,2 ]
Yao, Yuan [3 ]
Shan, Weifeng [4 ]
Yuan, Jing [5 ]
Xiao, Jian [1 ]
Ma, Yan [1 ]
Zhang, Kunpeng [6 ]
Wang, Zhibin [7 ,8 ]
机构
[1] Inst Disaster Prevent, Sch Emergency Management, Langfang 065201, Peoples R China
[2] Nat Acad Governance, Party Sch, Cent Comm CPC, Natl Inst Emergency Management, Beijing 100089, Peoples R China
[3] China Met Geol Bur, Inst Mineral Resources Res, Beijing 101300, Peoples R China
[4] Inst Disaster Prevent, Inst Intelligent Emergency Informat Proc, Langfang 065201, Peoples R China
[5] Inst Disaster Prevent, Sch Informat Engn, Langfang 065201, Peoples R China
[6] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[7] Gientech Digital Technol Grp Co Ltd, Beijing 100192, Peoples R China
[8] C4,Dongsheng Sci & Technol Pk,66 Xixiaokou Rd, Beijing 100192, Peoples R China
关键词
metaheuristic algorithms; Crested Porcupine Optimizer (CPO); composite Cauchy mutation strategy; adaptive dynamic adjustment strategy; population mutation strategy; META-HEURISTIC ALGORITHM; SWARM; INTELLIGENCE; EVOLUTIONARY; DESIGN; TESTS;
D O I
10.3390/math12193080
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.
引用
收藏
页数:41
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