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

This 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 gener...

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Autores:
Pimentel, Jairo
Ardila Hernandez, Carlos Julio
Niño, Elías
Jabba Molinares, Daladier
Ruiz-Rangel, Jonathan
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/3973
Acceso en línea:
https://hdl.handle.net/20.500.12442/3973
Palabra clave:
Finite Deterministic Automaton
Genetic Algorithm
Water Cycle Algorithm
Travelling Salesman Problem
Rights
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/3973
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.eng.fl_str_mv Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
title Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
spellingShingle Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
Finite Deterministic Automaton
Genetic Algorithm
Water Cycle Algorithm
Travelling Salesman Problem
title_short Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
title_full Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
title_fullStr Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
title_full_unstemmed Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
title_sort Water cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problem
dc.creator.fl_str_mv Pimentel, Jairo
Ardila Hernandez, Carlos Julio
Niño, Elías
Jabba Molinares, Daladier
Ruiz-Rangel, Jonathan
dc.contributor.author.none.fl_str_mv Pimentel, Jairo
Ardila Hernandez, Carlos Julio
Niño, Elías
Jabba Molinares, Daladier
Ruiz-Rangel, Jonathan
dc.subject.eng.fl_str_mv Finite Deterministic Automaton
Genetic Algorithm
Water Cycle Algorithm
Travelling Salesman Problem
topic Finite Deterministic Automaton
Genetic Algorithm
Water Cycle Algorithm
Travelling Salesman Problem
description This 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.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-09-13T22:13:10Z
dc.date.available.none.fl_str_mv 2019-09-13T22:13:10Z
dc.date.issued.none.fl_str_mv 2019
dc.type.eng.fl_str_mv article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.none.fl_str_mv 09740635
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/3973
identifier_str_mv 09740635
url https://hdl.handle.net/20.500.12442/3973
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_16ec
dc.publisher.eng.fl_str_mv International Journal of Artificial Intelligence
dc.source.eng.fl_str_mv Vol. 17 No. 2 (2019) October
International Journal of Artificial Intelligence
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv www.ceser.in/ceserp/index.php/ijai/article/view/6256
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/bc75e756-a4a8-4b27-96e8-a7f25dbe03b3/download
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spelling Pimentel, Jairo60583031-ac62-4348-8353-1507b0ea4068Ardila Hernandez, Carlos Julio6376bedb-3192-4777-9e34-c269ee81d567Niño, Elíasa6c57034-2f68-46b9-9038-cee1e9608044Jabba Molinares, Daladiera6449168-983a-4292-aef6-a59465add02aRuiz-Rangel, Jonathan6bcede93-7c56-43e9-af54-4ace50e87aad2019-09-13T22:13:10Z2019-09-13T22:13:10Z201909740635https://hdl.handle.net/20.500.12442/3973This 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.engInternational Journal of Artificial IntelligenceAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_16ecVol. 17 No. 2 (2019) OctoberInternational Journal of Artificial Intelligencewww.ceser.in/ceserp/index.php/ijai/article/view/6256Finite Deterministic AutomatonGenetic AlgorithmWater Cycle AlgorithmTravelling Salesman ProblemWater cycle algorithm: implementation and analysis of solutions to the bi-bjective travelling salesman problemarticlehttp://purl.org/coar/resource_type/c_6501Bianchi, 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.Bozorg 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.Butterworth-Heinemann 2019. Nature-inspired optimization algorithms for fuzzy controlled servo systems, Elsevier, Oxford, UK 8(11047): 239–287.Cagnina, 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.Eskandar, 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: 151–166Heidari, 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.Kallrath, 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.Ke, 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.Larra˜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.Lopez-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.Montemanni, 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.Moujahid, 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.Osaba, 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.Paquete, 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.Reinelt, G. 1991. Tspliba traveling salesman problem library, ORSA Journal on Computing, Operations Research Society of America, INFORMS 3(4): 376–384.Sadollah, 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.Sadollah, 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.Sadollah, 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.Sadollah, A., Eskandar, H., Lee, H. M. and Yoo, D. G. 2016. Water cycle algorithm: A detailed standard code, SoftwareX 5: 37–43.Sadollah, 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.Tenne, 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.The Traveling Salesman Problem 02 of November of 2016. http://www.math.uwaterloo.ca/tsp/apps/index.html.Umbarkar, 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.Vaˇsˇc´ak-J´an 2012. Adaptation of fuzzy cognitive maps by migration algorithms, Emerald Group Publishing Limited 41(3-4): 429–443.Vrkalovic, 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.Zhang, 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.Zitzler, 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.Hasanien, 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.CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/bc75e756-a4a8-4b27-96e8-a7f25dbe03b3/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/805afe67-eee4-460b-9d4d-7a9dda723345/download3fdc7b41651299350522650338f5754dMD5320.500.12442/3973oai:bonga.unisimon.edu.co:20.500.12442/39732024-08-14 21:52:30.939http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalmetadata.onlyhttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4=