Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

dc.creatorHu, Hanyu
dc.creatorShan, Weifeng
dc.creatorTang, Yixiang
dc.creatorAsghar Heidari, Ali
dc.creatorChen, Huiling
dc.creatorLiu, Haijun
dc.creatorWang, Maofa
dc.creatorEscorcia-Gutierrez, José
dc.creatorMansour, Romany F
dc.creatorChen, Jun
dc.date2023-05-12T21:53:04Z
dc.date2023-05-12T21:53:04Z
dc.date2022
dc.date.accessioned2023-10-03T20:08:08Z
dc.date.available2023-10-03T20:08:08Z
dc.identifierHanyu Hu, Weifeng Shan, Yixiang Tang, Ali Asghar Heidari, Huiling Chen, Haijun Liu, Maofa Wang, José Escorcia-Gutierrez, Romany F Mansour, Jun Chen, Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection, Journal of Computational Design and Engineering, Volume 9, Issue 6, December 2022, Pages 2524–2555, https://doi.org/10.1093/jcde/qwac119
dc.identifier2288-4300
dc.identifierhttps://hdl.handle.net/11323/10112
dc.identifier10.1093/jcde/qwac119
dc.identifier2288-5048
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174436
dc.descriptionThe sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.
dc.format32 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherOxford University Press
dc.publisherUnited Kingdom
dc.relationJournal of Computational Design and Engineering
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dc.rightsCopyright © 2023 Society for Computational Design and Engineering
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
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dc.sourcehttps://academic.oup.com/jcde/article/9/6/2524/6795289
dc.subjectSine cosine algorithm
dc.subjectFeature selection
dc.subjectGlobal optimization
dc.subjectMetaheuristic algorithms
dc.titleHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
dc.typeArtículo de revista
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