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

The 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...

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Autores:
Hu, Hanyu
Shan, Weifeng
Tang, Yixiang
Asghar Heidari, Ali
Chen, Huiling
Liu, Haijun
Wang, Maofa
Escorcia-Gutierrez, José
Mansour, Romany F
Chen, Jun
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10112
Acceso en línea:
https://hdl.handle.net/11323/10112
https://repositorio.cuc.edu.co/
Palabra clave:
Sine cosine algorithm
Feature selection
Global optimization
Metaheuristic algorithms
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_01c7eef396812d9d6c7b9794acea956a
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10112
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
title Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
spellingShingle Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
Sine cosine algorithm
Feature selection
Global optimization
Metaheuristic algorithms
title_short Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
title_full Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
title_fullStr Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
title_full_unstemmed Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
title_sort Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
dc.creator.fl_str_mv Hu, Hanyu
Shan, Weifeng
Tang, Yixiang
Asghar Heidari, Ali
Chen, Huiling
Liu, Haijun
Wang, Maofa
Escorcia-Gutierrez, José
Mansour, Romany F
Chen, Jun
dc.contributor.author.none.fl_str_mv Hu, Hanyu
Shan, Weifeng
Tang, Yixiang
Asghar Heidari, Ali
Chen, Huiling
Liu, Haijun
Wang, Maofa
Escorcia-Gutierrez, José
Mansour, Romany F
Chen, Jun
dc.subject.proposal.eng.fl_str_mv Sine cosine algorithm
Feature selection
Global optimization
Metaheuristic algorithms
topic Sine cosine algorithm
Feature selection
Global optimization
Metaheuristic algorithms
description The 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.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-05-12T21:53:04Z
dc.date.available.none.fl_str_mv 2023-05-12T21:53:04Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.identifier.citation.spa.fl_str_mv Hanyu 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.identifier.issn.spa.fl_str_mv 2288-4300
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10112
dc.identifier.doi.none.fl_str_mv 10.1093/jcde/qwac119
dc.identifier.eissn.spa.fl_str_mv 2288-5048
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Hanyu 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
2288-4300
10.1093/jcde/qwac119
2288-5048
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10112
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Journal of Computational Design and Engineering
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spelling Atribución 4.0 Internacional (CC BY 4.0)Copyright © 2023 Society for Computational Design and Engineeringhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hu, HanyuShan, WeifengTang, YixiangAsghar Heidari, AliChen, HuilingLiu, HaijunWang, MaofaEscorcia-Gutierrez, JoséMansour, Romany FChen, Jun2023-05-12T21:53:04Z2023-05-12T21:53:04Z2022Hanyu 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/qwac1192288-4300https://hdl.handle.net/11323/1011210.1093/jcde/qwac1192288-5048Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.32 páginasapplication/pdfengOxford University PressUnited Kingdomhttps://academic.oup.com/jcde/article/9/6/2524/6795289Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selectionArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Journal of Computational Design and EngineeringAbd Elaziz, M., Oliva, D., & Xiong, S. (2017). 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IOP Conference Series: Materials Science and Engineering, 366(1), 012005. https://doi.org/10.1088/1757-899X/366/1/012005.2555252429Sine cosine algorithmFeature selectionGlobal optimizationMetaheuristic algorithmsPublicationORIGINALHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdfHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdfArtículoapplication/pdf3727312https://repositorio.cuc.edu.co/bitstreams/0f5b0e41-473d-494d-90bd-46f0251241b4/download15cdfa90f683ad83b09933ce86c1eaa5MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/73afdc8d-9e4f-4ba3-af0d-2360c15ec6cf/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdf.txtHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdf.txtExtracted texttext/plain146591https://repositorio.cuc.edu.co/bitstreams/8795e557-38a6-4c83-b8dd-ffda3d76d7eb/downloaddf76e3c2eda0082d2e919e661d79a056MD53THUMBNAILHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdf.jpgHorizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection.pdf.jpgGenerated Thumbnailimage/jpeg14570https://repositorio.cuc.edu.co/bitstreams/fd3687f7-73d2-4639-a755-ef92794080f6/download9fd23c95b9cea2df69dc75eec1ab6563MD5411323/10112oai:repositorio.cuc.edu.co:11323/101122024-09-17 14:23:35.011https://creativecommons.org/licenses/by/4.0/Copyright © 2023 Society for Computational Design and Engineeringopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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