Logotipo del repositorio
Logotipo del repositorio
 

Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem

Vista sencilla de ítem
dc.contributor.authorPimentel, Jairo
dc.contributor.authorArdila Hernandez, Carlos Julio
dc.contributor.authorNiño, Elías
dc.contributor.authorJabba Molinares, Daladier
dc.contributor.authorRuiz-Rangel, Jonathan
dc.date.accessioned2019-09-13T22:13:10Z
dc.date.available2019-09-13T22:13:10Z
dc.date.issued2019
dc.description.abstractThis research is an implementation of the Water Cycle Algorithm (WCA) to solve the biobjective Travelling Salesman Problem, based on the kroAB100 problem in the TSPLIB library, and compare its performance to an alternative metaheuristic algorithm (MO Ant Colony BiCriterionAnt). Metrics such as generational distance, inverse generational distance, spacing, dispersion and maximum dispersion were used to compare the two algorithms. Results demonstrate that the Water Cycle Algorithm generates superior solutions to this category of problem according to most of the metrics.eng
dc.identifier.issn09740635
dc.identifier.urihttps://hdl.handle.net/20.500.12442/3973
dc.language.isoengeng
dc.publisherInternational Journal of Artificial Intelligenceeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceVol. 17 No. 2 (2019) Octobereng
dc.sourceInternational Journal of Artificial Intelligenceeng
dc.source.uriwww.ceser.in/ceserp/index.php/ijai/article/view/6256eng
dc.subjectFinite Deterministic Automatoneng
dc.subjectGenetic Algorithmeng
dc.subjectWater Cycle Algorithmeng
dc.subjectTravelling Salesman Problemeng
dc.titleWater cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problemeng
dc.typearticleeng
dcterms.referencesBianchi, L., Dorigo, M., Gambardella, L. M. and Gutjahr, W. J. 2009. A survey on metaheuristics for stochastic combinatorial optimization, Nat Comput, Springer, Netherlands 8(11047): 239–287.eng
dcterms.referencesBozorg Haddad, O., Moravej, M. and Lo´ aiciga, H. A. 2015. Application of the water cycle algorithm to the optimal operation of reservoir systems, Journal of Irrigation and Drainage Engineering, American Society of Civil Engineers 142(5): 4001–4064.eng
dcterms.referencesButterworth-Heinemann 2019. Nature-inspired optimization algorithms for fuzzy controlled servo systems, Elsevier, Oxford, UK 8(11047): 239–287.eng
dcterms.referencesCagnina, L. C. 2010. Optimización Mono y Multiobjetivo a través de una Heurística de Inteligencia Colectiva, PhD thesis, Universidad Nacional de San Luis,San Luis, Argentina.eng
dcterms.referencesEskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M. 2012. Water cycle algorithm a novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers and Structures 110111: 151–166eng
dcterms.referencesHasanien, H. M. and Matar, M. 2018. Water cycle algorithm-based optimal control strategy for efficient operation of an autonomous microgrid, IET Generation Transmission and Distribution 12(21): 5739–5746.spa
dcterms.referencesHeidari, A. A., Abbaspour, R. A. and Jordehi, A. R. 2017. An efficient chaotic water cycle algorithm for optimization tasks, Neural Computing and Applications, Springer, London 28(1): 57–85.eng
dcterms.referencesKallrath, J., Rebennack, S., Kallrath, J. and Kusche, R. 2014. Solving real-world cutting stockproblems in the paper industry: Mathematical approaches, experience and challenges, European Journal of Operational Research 238(1): 374–389.eng
dcterms.referencesKe, L., Zhang, Q. and Battiti, R. 2013. Moea/d-aco: A multiobjective evolutionary algorithm using decomposition and ant colony, IEEE Transactions on Cybernetics 43(6): 1845–1859.eng
dcterms.referencesLarra˜naga, P., Kuijpers, C. M., Murga, R. H., Inza, I. and Dizdarevic, S. 1999. Genetic algorithms for the travelling salesman problem: A review of representations and operators, Artificial Intelligence Review: An International Science and Engineering Journal 13(2): 129–170.eng
dcterms.referencesLopez-Ibanez, M. and Stuetzle, T. 2012. The automatic design of multi-objective ant colony optimization algorithms, IEEE Transactions on Evolutionary Computation, the IRIDIA laboratory, CoDE, Universite libre de Bruxelles, 1050 Brussels, Belgium 16(6): 861–875.eng
dcterms.referencesMontemanni, R., Barta, J., Mastrolilli, M. and Gambardell, L. M. 2007. The robust traveling salesman problem with interval data, Institute for Operations Research and the Management Sciences, INFORMS 41(3): 366–381.eng
dcterms.referencesMoujahid, A., In ˜ aki, I. and Larra˜naga, P. 2008. Tema 2. algoritmos gen´ eticos, Departamento de Ciencias de la Computaci´on e Inteligencia Artificial, Universidad del Pa´ıs, VascoEuskal Herriko Unibertsitatea pp. 1–33.eng
dcterms.referencesOsaba, E., Del Ser, J., Sadollah, A., Nekane, B. M. and Camacho, D. 2018. A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem, Applied Soft Computing 71: 277–290.eng
dcterms.referencesPaquete, L., Chiarandini, M. and Stutzle, T. 2004. Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study, Metaheuristics for Multiobjective Optimisation: Lecture Notes in Economics and Mathematical Systems, Springer, Berlin, Heidelberg 35: 177–199.eng
dcterms.referencesReinelt, G. 1991. Tspliba traveling salesman problem library, ORSA Journal on Computing, Operations Research Society of America, INFORMS 3(4): 376–384.eng
dcterms.referencesSadollah, A., Eskandar, H. and Bahreininejad, A. 2013. Weight optimization of truss structures using water cycle algorithm, International journal of optimization in civil engineering,Faculty of Engineering, Semnan University, Semnan, Iran 3(1): 115–129.eng
dcterms.referencesSadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J. H. 2015a. Water cycle algorithm for solving multi-objective optimization problems, Soft Computing, Springer, Berlin, Heidelberg 19(9): 2587–2603.eng
dcterms.referencesSadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J. H. 2015b. Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems, Applied Soft Computing 30: 58–71.eng
dcterms.referencesSadollah, A., Eskandar, H., Lee, H. M. and Yoo, D. G. 2016. Water cycle algorithm: A detailed standard code, SoftwareX 5: 37–43.eng
dcterms.referencesSadollah, A., Kim, J. H., Eskandar, H. and Yoo, D. G. 2013. Sizing optimization of sandwich panels having prismatic core using water cycle algorithm, Intelligent Systems (GCIS), 2013 Fourth Global Congress on IEEE, IEEE Conference, Hong Kong, China pp. 325–328.eng
dcterms.referencesTenne, Y. and Goh, C. 2010. Computational intelligence in optimization: applications and implementations, Adaptation, Learning, and Optimization (Book 7), Springer-Verlan, Berlin, Heidelberg 7: 359–380.eng
dcterms.referencesThe Traveling Salesman Problem 02 of November of 2016. http://www.math.uwaterloo.ca/tsp/apps/index.html.eng
dcterms.referencesUmbarkar, A. J. and Sheth, P. D. 2015. Crossover operators in genetic algorithms: a review, ICTACT International Journal of Computer Applications, Foundation of Computer Science (FCS), NY, USA 6(1): 1083–1092.eng
dcterms.referencesVaˇsˇc´ak-J´an 2012. Adaptation of fuzzy cognitive maps by migration algorithms, Emerald Group Publishing Limited 41(3-4): 429–443.eng
dcterms.referencesVrkalovic, S., Lunca, E. C. and Borlea, I. D. 2018. Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants, International Journal of Artificial Intelligence 16(2): 208–222.eng
dcterms.referencesZhang, Z., Gao, C., Lu, Y., Liu, Y. and Liang, M. 2016. Multi-objective ant colony optimization based on the physarum-inspired mathematical model for bi-objective traveling salesman problems, PLoS ONE, Kyushu University, JAPAN 11(1): 1–23.eng
dcterms.referencesZitzler, E., Deb, K. and Thiele, L. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary computation, MIT Press Cambridge, MA, USA 8(2): 173– 195.eng

Archivos

Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
368 B
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Artículos

Universidad Simón Bolívar: Todos los derechos reservados / Sistema de biblioteca