Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems

Autores:: Cobos-Lozada, Carlos-Alberto
Muñoz-Collazos, Henry
Urbano-Muñoz, Richar

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
title	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
spellingShingle	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems Artificial intelligence Cuckoo search algorithm Large-scale continuous problems Metaheuristics Optimization Algoritmo de búsqueda del cuco Inteligencia artificial Metaheurísticas Optimización Problemas continuos a gran escala
title_short	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
title_full	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
title_fullStr	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
title_full_unstemmed	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
title_sort	Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems
dc.creator.fl_str_mv	Cobos-Lozada, Carlos-Alberto Muñoz-Collazos, Henry Urbano-Muñoz, Richar
dc.contributor.author.eng.fl_str_mv	Cobos-Lozada, Carlos-Alberto Muñoz-Collazos, Henry Urbano-Muñoz, Richar
dc.subject.none.fl_str_mv	Artificial intelligence Cuckoo search algorithm Large-scale continuous problems Metaheuristics Optimization Algoritmo de búsqueda del cuco Inteligencia artificial Metaheurísticas Optimización Problemas continuos a gran escala
topic	Artificial intelligence Cuckoo search algorithm Large-scale continuous problems Metaheuristics Optimization Algoritmo de búsqueda del cuco Inteligencia artificial Metaheurísticas Optimización Problemas continuos a gran escala
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-08-29 21:27:51 2026-04-16T17:47:34Z
dc.date.available.none.fl_str_mv	2024-08-29 21:27:51
dc.date.issued.none.fl_str_mv	2024-08-29
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dc.relation.references.none.fl_str_mv	L. Velasco, H. Guerrero, A. Hospitaler, “A Literature Review and Critical Analysis of Metaheuristics Recently Developed,” Arch. Comput. Methods Eng., 2023. https://doi.org/10.1007/s11831-023-09975-0 R. R. Abo-Alsabeh, A. Salhi, “The Genetic Algorithm: A study survey,” Iraqi J. Sci., vol. 63, no. 3, pp. 1215-1231, 2022. https://doi.org/10.24996/ijs.2022.63.3.27 F. Neri, C. Cotta, “Memetic algorithms and memetic computing optimization: A literature review,” Swarm Evol. Comput., vol. 2, pp. 1-14, 2012. https://doi.org/10.1016/j.swevo.2011.11.003 V. K. Prajapati, M. Jain, L. Chouhan, “Tabu Search Algorithm (TSA): A Comprehensive Survey,” in Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, 2020, pp. 222–229. https://doi.org/10.1109/ICETCE48199.2020.9091743 N. Nayar, S. Gautam, P. Singh, G. Mehta, “Ant Colony Optimization: A Review of Literature and Application in Feature Selection,” in Lecture Notes in Networks and Systems, 2021, pp. 285-297. https://doi.org/10.1007/978-981-33-4305-4_22 A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 2531-2561, 2022. https://doi.org/10.1007/s11831-021-09694-4 I. Sharma, V. Kumar, S. Sharma, “A Comprehensive Survey on Grey Wolf Optimization,” Recent Adv. Comput. Sci. Commun., vol. 15, no. 3, pp. 323-333, 2022. https://doi.org/10.2174/2666255813999201007165454 J. Li, X. Wei, B. Li, Z. Zeng, “A survey on firefly algorithms,” Neurocomputing, vol. 500, pp. 662-678, 2022. https://doi.org/10.1016/j.neucom.2022.05.100 D. Karaboga, B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108-132, 2009. https://doi.org/10.1016/j.amc.2009.03.090 M. F. Ahmad, N. A. M. Isa, W. H. Lim, K. M. Ang, “Differential evolution: A recent review based on state-of-the-art works,” Alexandria Eng. J., vol. 61, no. 5, pp. 3831-3872, 2022. https://doi.org/10.1016/j.aej.2021.09.013 F. Qin, A. M. Zain, K.-Q. Zhou, “Harmony search algorithm and related variants: A systematic review,” Swarm Evol. Comput., vol. 74, 2022. https://doi.org/10.1016/j.swevo.2022.101126 E. Ruano-Daza, C. Cobos, J. Torres-Jimenez, M. Mendoza, A. Paz, “A multiobjective bilevel approach based on global-best harmony search for defining optimal routes and frequencies for bus rapid transit systems,” Appl. Soft Comput. J., vol. 67, pp. 567-583, 2018. https://doi.org/10.1016/j.asoc.2018.03.026 S. Salhi, J. Thompson, “An overview of heuristics and metaheuristics,” in The Palgrave Handbook of Operations Research, 2022, pp. 353-403. https://doi.org/10.1007/978-3-030-96935-6_11 X.-S. Yang, S. Deb, “Cuckoo Search via Lévy flights,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009, pp. 210-214. https://doi.org/10.1109/NABIC.2009.5393690 K. Safdar, K. N. Abdul Rani, H. A. Rahim, S. J. Rosli, M. A. Jamlos, “A Review on Research Trends in using Cuckoo Search Algorithm: Applications and Open Research Challenges,” Prz. Elektrotechniczny, vol. 1, no. 5, pp. 18-24, 2023. https://doi.org/10.15199/48.2023.05.04 E. Valian, S. Mohanna, S. Tavakoli, “Improved Cuckoo Search Algorithm for Feed forward Neural Network Training,” Int. J. Artif. Intell. Appl., vol. 2, no. 3, pp. 36-43, 2011. https://doi.org/10.5121/ijaia.2011.2304 S. Walton, O. Hassan, K. Morgan, M. R. Brown, “Modified cuckoo search: A new gradient free optimisation algorithm,” Chaos, Solitons and Fractals, vol. 44, no. 9, pp. 710-718, 2011. https://doi.org/101016/jchaos201106004 M. Tuba, M. Subotic, N. Stanarevic, “Modified Cuckoo Search Algorithm for Unconstrained Optimization Problems,” in Proc. 5th Eur. Conf. Eur. Comput. Conf., pp. 263-268, 2011. http://www.wseas.us/e-library/conferences/2011/Paris/ECC/ECC-43.pdf R. Rajabioun, “Cuckoo Optimization Algorithm,” Appl. Soft Comput., vol. 11, no. 8, pp. 5508-5518, 2011. https://doi.org/10.1016/j.asoc.2011.05.008 J. Li, Q. An, H. Lei, Q. Deng, G.-G. Wang, “Survey of Lévy Flight-Based Metaheuristics for Optimization,” Mathematics, vol. 10, no. 15, e2785, 2022. https://doi.org/10.3390/math10152785 R. S. Michalski, “Learnable evolution model: evolutionary processes guided by machine learning,” Mach. Learn., vol. 38, no. 1, pp. 9-40, 2000. https://doi.org/10.1023/a:1007677805582 A. L. da Costa Oliveira, A. Britto, R. Gusmão, “Machine learning enhancing metaheuristics: a systematic review,” Soft Comput., 2023. https://doi.org/10.1007/s00500-023-08886-3 C. Cobos, D. Estupiñán, J. Pérez, “GHS + LEM: Global-best Harmony Search using learnable evolution models,” Appl. Math. Comput., vol. 218, no. 6, pp. 2558-2578, 2011. https://doi.org/10.1016/j.amc.2011.07.073 P. N. Suganthan et al., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Singapore, 2005. http://www.cmap.polytechnique.fr/~nikolaus.hansen/Tech-Report-May-30-05.pdf K. Tang et al., “Benchmark functions for the CEC’2008 special session and competition on large scale global optimization,” in IEEE World Congress on Computational Intelligence, Rio de Janeiro, Brazil: IEEE, 2008, pp. 1-18. http://sci2s.ugr.es/programacion/workshop/Tech.Report.CEC2008.LSGO.pdf T. Ke, L. Xiaodong, S. P. N., Y. Zhenyu, W. Thomas, “Benchmark Functions for the CEC’2010 Special Session and Competition on Large-Scale Global Optimization,” Shanghai, China, 2010. http://goanna.cs.rmit.edu.au/~xiaodong/cec13-lsgo/competition/cec2013-lsgo-benchmark-tech-report.pdf M. Molga, C. Smutnicki, “Test functions for optimization needs,” Test functions for optimization needs, pp. 1-43, 2005. https://robertmarks.org/Classes/ENGR5358/Papers/functions.pdf D. H. Wolpert, W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67-82, 1997. https://doi.org/10.1109/4235.585893
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dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17895/14941 https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17895/14531
dc.relation.citationedition.eng.fl_str_mv	Núm. 69 , Año 2024 : July-September 2024: Open Science at the Service of Engineering
dc.rights.none.fl_str_mv	Carlos-Alberto Cobos-Lozada, Henry Muñoz-Collazos, Richar Urbano-Muñoz - 2024
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spelling	Cobos-Lozada, Carlos-AlbertoMuñoz-Collazos, HenryUrbano-Muñoz, Richar2024-08-29 21:27:512026-04-16T17:47:34Z2024-08-29 21:27:512024-08-290121-1129https://repositorio.uptc.edu.co/handle/001/2158010.19053/uptc.01211129.v33.n69.2024.178952357-5328https://doi.org/10.19053/uptc.01211129.v33.n69.2024.17895text/xmlapplication/pdfUniversidad Pedagógica y Tecnológica de ColombiaCarlos-Alberto Cobos-Lozada, Henry Muñoz-Collazos, Richar Urbano-Muñoz - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/17895Artificial intelligenceCuckoo search algorithmLarge-scale continuous problemsMetaheuristicsOptimizationAlgoritmo de búsqueda del cucoInteligencia artificialMetaheurísticasOptimizaciónProblemas continuos a gran escalaComparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization ProblemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85L. Velasco, H. Guerrero, A. Hospitaler, “A Literature Review and Critical Analysis of Metaheuristics Recently Developed,” Arch. Comput. Methods Eng., 2023. https://doi.org/10.1007/s11831-023-09975-0R. R. Abo-Alsabeh, A. Salhi, “The Genetic Algorithm: A study survey,” Iraqi J. Sci., vol. 63, no. 3, pp. 1215-1231, 2022. https://doi.org/10.24996/ijs.2022.63.3.27F. Neri, C. Cotta, “Memetic algorithms and memetic computing optimization: A literature review,” Swarm Evol. Comput., vol. 2, pp. 1-14, 2012. https://doi.org/10.1016/j.swevo.2011.11.003V. K. Prajapati, M. Jain, L. Chouhan, “Tabu Search Algorithm (TSA): A Comprehensive Survey,” in Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, 2020, pp. 222–229. https://doi.org/10.1109/ICETCE48199.2020.9091743N. Nayar, S. Gautam, P. Singh, G. Mehta, “Ant Colony Optimization: A Review of Literature and Application in Feature Selection,” in Lecture Notes in Networks and Systems, 2021, pp. 285-297. https://doi.org/10.1007/978-981-33-4305-4_22A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 2531-2561, 2022. https://doi.org/10.1007/s11831-021-09694-4I. Sharma, V. Kumar, S. Sharma, “A Comprehensive Survey on Grey Wolf Optimization,” Recent Adv. Comput. Sci. Commun., vol. 15, no. 3, pp. 323-333, 2022. https://doi.org/10.2174/2666255813999201007165454J. Li, X. Wei, B. Li, Z. Zeng, “A survey on firefly algorithms,” Neurocomputing, vol. 500, pp. 662-678, 2022. https://doi.org/10.1016/j.neucom.2022.05.100D. Karaboga, B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108-132, 2009. https://doi.org/10.1016/j.amc.2009.03.090M. F. Ahmad, N. A. M. Isa, W. H. Lim, K. M. Ang, “Differential evolution: A recent review based on state-of-the-art works,” Alexandria Eng. J., vol. 61, no. 5, pp. 3831-3872, 2022. https://doi.org/10.1016/j.aej.2021.09.013F. Qin, A. M. Zain, K.-Q. Zhou, “Harmony search algorithm and related variants: A systematic review,” Swarm Evol. Comput., vol. 74, 2022. https://doi.org/10.1016/j.swevo.2022.101126E. Ruano-Daza, C. Cobos, J. Torres-Jimenez, M. Mendoza, A. Paz, “A multiobjective bilevel approach based on global-best harmony search for defining optimal routes and frequencies for bus rapid transit systems,” Appl. Soft Comput. J., vol. 67, pp. 567-583, 2018. https://doi.org/10.1016/j.asoc.2018.03.026S. Salhi, J. Thompson, “An overview of heuristics and metaheuristics,” in The Palgrave Handbook of Operations Research, 2022, pp. 353-403. https://doi.org/10.1007/978-3-030-96935-6_11X.-S. Yang, S. Deb, “Cuckoo Search via Lévy flights,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009, pp. 210-214. https://doi.org/10.1109/NABIC.2009.5393690K. Safdar, K. N. Abdul Rani, H. A. Rahim, S. J. Rosli, M. A. Jamlos, “A Review on Research Trends in using Cuckoo Search Algorithm: Applications and Open Research Challenges,” Prz. Elektrotechniczny, vol. 1, no. 5, pp. 18-24, 2023. https://doi.org/10.15199/48.2023.05.04E. Valian, S. Mohanna, S. Tavakoli, “Improved Cuckoo Search Algorithm for Feed forward Neural Network Training,” Int. J. Artif. Intell. Appl., vol. 2, no. 3, pp. 36-43, 2011. https://doi.org/10.5121/ijaia.2011.2304S. Walton, O. Hassan, K. Morgan, M. R. Brown, “Modified cuckoo search: A new gradient free optimisation algorithm,” Chaos, Solitons and Fractals, vol. 44, no. 9, pp. 710-718, 2011. https://doi.org/101016/jchaos201106004M. Tuba, M. Subotic, N. Stanarevic, “Modified Cuckoo Search Algorithm for Unconstrained Optimization Problems,” in Proc. 5th Eur. Conf. Eur. Comput. Conf., pp. 263-268, 2011. http://www.wseas.us/e-library/conferences/2011/Paris/ECC/ECC-43.pdfR. Rajabioun, “Cuckoo Optimization Algorithm,” Appl. Soft Comput., vol. 11, no. 8, pp. 5508-5518, 2011. https://doi.org/10.1016/j.asoc.2011.05.008J. Li, Q. An, H. Lei, Q. Deng, G.-G. Wang, “Survey of Lévy Flight-Based Metaheuristics for Optimization,” Mathematics, vol. 10, no. 15, e2785, 2022. https://doi.org/10.3390/math10152785R. S. Michalski, “Learnable evolution model: evolutionary processes guided by machine learning,” Mach. Learn., vol. 38, no. 1, pp. 9-40, 2000. https://doi.org/10.1023/a:1007677805582A. L. da Costa Oliveira, A. Britto, R. Gusmão, “Machine learning enhancing metaheuristics: a systematic review,” Soft Comput., 2023. https://doi.org/10.1007/s00500-023-08886-3C. Cobos, D. Estupiñán, J. Pérez, “GHS + LEM: Global-best Harmony Search using learnable evolution models,” Appl. Math. Comput., vol. 218, no. 6, pp. 2558-2578, 2011. https://doi.org/10.1016/j.amc.2011.07.073P. N. Suganthan et al., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Singapore, 2005. http://www.cmap.polytechnique.fr/~nikolaus.hansen/Tech-Report-May-30-05.pdfK. Tang et al., “Benchmark functions for the CEC’2008 special session and competition on large scale global optimization,” in IEEE World Congress on Computational Intelligence, Rio de Janeiro, Brazil: IEEE, 2008, pp. 1-18. http://sci2s.ugr.es/programacion/workshop/Tech.Report.CEC2008.LSGO.pdfT. Ke, L. Xiaodong, S. P. N., Y. Zhenyu, W. Thomas, “Benchmark Functions for the CEC’2010 Special Session and Competition on Large-Scale Global Optimization,” Shanghai, China, 2010. http://goanna.cs.rmit.edu.au/~xiaodong/cec13-lsgo/competition/cec2013-lsgo-benchmark-tech-report.pdfM. Molga, C. Smutnicki, “Test functions for optimization needs,” Test functions for optimization needs, pp. 1-43, 2005. https://robertmarks.org/Classes/ENGR5358/Papers/functions.pdfD. H. Wolpert, W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67-82, 1997. https://doi.org/10.1109/4235.585893e1789569e1789533Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/17895/14941https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17895/14531Núm. 69 , Año 2024 : July-September 2024: Open Science at the Service of EngineeringPublicationOREORE.xmltext/xml2736https://repositorio.uptc.edu.co/bitstreams/5fbc2bce-43a1-44c2-9b00-602462f07712/download38139fabe48ad66783e91361da946523MD51falseAnonymousREAD001/21580oai:repositorio.uptc.edu.co:001/215802026-10-29 14:05:27.444https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Comparative Study of Cuckoo-Inspired Algorithms to Solve Large-Scale Continuous Optimization Problems

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