Solving Travelling Salesman Problem: a Hybrid Optimization Algorithm

dc.authorscopusid 57214822052
dc.contributor.author Sozen,N.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:45:23Z
dc.date.available 2024-07-05T15:45:23Z
dc.date.issued 2019
dc.department Atılım University en_US
dc.department-temp Sozen N., Atilim University, Computer Engineering, Ankara, Turkey en_US
dc.description.abstract There are numerous selection, crossover, and mutation methods suggested in the literature when it comes to genetic algorithms. However, behavior of each different method changes when used in combination with other methods. In this paper, a brief and clear explanation of many popular selection, crossover and mutation techniques has been presented and the combination of various optimization methods using Genetic Algorithm has been implemented to generate a hybrid algorithm as a solution to the well-known NP-hard Travelling Salesman Problem (TSP). In this study, 10 different hybrid algorithms are implemented and experimented. Each of these algorithms are formed combining two different selection methods, 3 different crossover methods and 2 different mutation methods. Each of the ten different algorithms have been implemented and their performance have been tested with two different datasets to understand which algorithm outperforms the others. Performance of the combination of various methods have been presented and the findings illustrated that combination of specific crossover, selection and mutation methods outperform in terms of the ultimate optimal result. The results have been compared with the algorithms in the literature that combines Roulette Wheel Selection (RWS), and Stochastic Universal Selection (SUS); each implemented in combination with Partially Mapped Crossover, Cycle Crossover, and Ordered Crossover. Each combination has been tried on various population sizes, mutation and crossover rates. It is found that combining specific selection, mutation and crossover methods can outperform the methods suggested in the literature in equal circumstances-when the same population size, generation size, mutation and crossover rates are used. © 2019 IEEE. en_US
dc.identifier.citationcount 2
dc.identifier.doi 10.1109/UBMYK48245.2019.8965478
dc.identifier.isbn 978-172813992-0
dc.identifier.scopus 2-s2.0-85079208956
dc.identifier.uri https://doi.org/10.1109/UBMYK48245.2019.8965478
dc.identifier.uri https://hdl.handle.net/20.500.14411/3912
dc.institutionauthor Sözen, Nergiz
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings -- 1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 157111 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject Genetic algorithms en_US
dc.subject hybrid algorithm en_US
dc.subject natural computing en_US
dc.subject travelling salesman problem en_US
dc.subject TSP en_US
dc.title Solving Travelling Salesman Problem: a Hybrid Optimization Algorithm en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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relation.isOrgUnitOfPublication.latestForDiscovery e0809e2c-77a7-4f04-9cb0-4bccec9395fa

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