The use of ant colony optimization algorithms for the problem of optimal route search

dc.citation.conferenceLitteris et Artibus
dc.contributor.affiliationLviv Polytechnic National Universityuk_UA
dc.contributor.authorRybchak, Zoriana
dc.contributor.authorLytvyn, Vasyl
dc.contributor.authorKunanets, Natalia
dc.coverage.countryUAuk_UA
dc.coverage.placenameLvivuk_UA
dc.date.accessioned2018-03-02T14:34:25Z
dc.date.available2018-03-02T14:34:25Z
dc.date.issued2015
dc.description.abstractThis paper introduces the ant colony system , a distributed algorithm that is applied to the traveling salesman problem In the ant colony system, a set of cooperating agents called ants cooperate to find good solutions to traveling salesman problems. Ant algorithms- a class meta heuristic methods solving combinatorial optimization problems. The basis of these algorithms responsible behavior of real ants in nature. Ants - a collective beings who build very complex social structure. Their ability to find optimal paths from nest to food sources has attracted the attention of scientists long ago. By submitting information to each other through chemicals including pheromone, ants form a chain of positive feedback. This relationship, in turn, leads to the fact that the ants eventually choose more optimal (short) path to the goal, although at the beginning there were many and they were very different.uk_UA
dc.format.pages62-65
dc.identifier.citationRybchak Z. The use of ant colony optimization algorithms for the problem of optimal route search / Zoriana Rybchak, Vasyl Lytvyn, Natalia Kunanets // Litteris et Artibus : proceedings of the 5th International youth science forum, November 26–28, 2015, Lviv, Ukraine / Lviv Polytechnic National University. – Lviv : Lviv Polytechnic Publishing House, 2015. – P. 62–65. – Bibliography: 19 titles.uk_UA
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/39500
dc.language.isoenuk_UA
dc.publisherLviv Polytechnic Publishing Houseuk_UA
dc.relation.referencesen[1] Dorigo M. Optimization, Learning and Natural Algorithms. PhD Thesis, Dipartimento di Elettronica, Politechnico Di Milano, Italy. – 2012. – 140 p. [Італійською]. [2] Dorigo M., Maniezzo V., Colorni A. The Ant System: Optimization by a Colony of Cooperating Agents // IEEE Trans. on Systems, Man and Cybernetics. Part B. – 2006. – № 1. – Vol. 26. – P. 29–41. [3] Bonavear E., Dorigo M. Swarm Intelligence: from Natural to Artificial Systems. – Oxford University Press, 2009. – 307 p. [4] Dorigo M. Swarm Intelligence, Ant Algorithms and Ant Colony Optimization // Reader for CEU Summer University Course «Complex System», Budapest, Central European University. – 2001. – P. 1–38. [5] Ant Colony Optimization: http://iridia. ulb. ac. be/~mdorigo/ACO/ACO. html [6] Goss S., Aron S., Deneubourg J. L., Pasteels J. M. Self-Organized Shortcuts in the Argentine Ant // Naturwissenshaften. – 2009. – № 76.– P. 579–581. [7] Bullnheimer B., Hartl R. F., Strauss C. A New Rank-Based Version of the Ant System: A Computational Study // Central European Journal for Operations Research and Economics. – 1999. – № 1(7). – P. 25–38. [8] Dorigo M., Gambardella L. M. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem // IEEE Trans. on Evolutionary Computation. – 2007. – № 1(1). – P. 53–66. [9] Stutzle T., Hoos H. H. MAX-MIN Ant System // Future Generation Computer Systems. – 2010. – № 8(16). – P. 889–914. [10] TSPLIB: http://www. iwr. uni-heidelberg. de/groups/comopt/software/TSPLIB95. [11] Reinelt G. The Traveling Salesman: Computational Solutions for TSP Applications. Lecture Notes in Computer Science. Vol. 840. – Berlin: Springler- Verlag. – 2004. [12] Пападимитриу Х., Стайглиц К. Комбинаторная оптимизация. Алгоритмы и сложность: Пер. с англ. – М.: Мир, 2005. – 512 с. [13] Reimann M. Ant Based Optimization in Good Transportation. PhD Thesis. University of Vienna. – Vienna, Austria. – 2002. – 149 p. [14] Socha K., Knowles J., Smples M. A MAX-MIN Ant System for the University Course Timetabling Problem. In Proc. of the Third International Workshop on Ant Algorithms «ANTS 2002». Lecture Notes in Computer Science 2463: Springer-Verlag, 2002. – P. 1–13. [15] Rodrigues A. Application of Ant Colony Optimization to Data Distribution in Memory in Computer Systems. In Abstracts of 7th Annual Swarm Researchers Meeting «SwarmFest 2003». USA, Notre Dame. – 2003 (повний текст на http://www. nd. edu/~arodrig6/). [16] Shmygelska A., Hoos H. An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem. In Proc. of the Third International Workshop on Ant Algorithms «ANTS 2002». Lecture Notes in Computer Science 2463: Springer-Verlag, 2002. [17] Mariano C. E., Morales E. MOAQ: An Ant-Q Algorithm for Multiple Objective Optimization Problems. In Proc. of Genetic and Evolutionary Computation Conference (GECCO-99). USA, San- Francisco, 2009, Vol. 1. – P. 894–901. [18] Штовба С. Д., Рудий О. М. Мурашині алгоритми оптимізації // Вісник ВПІ. – 2004. – № 4. – С. 62–69. [19] Колесніков К. В. Синектика натурних методів маршрутизації потоків даних у автономних системах телекомунікації / К. В. Колесніков, А. Р. Карапетян, О. В. Кравченко // Вісник ХНУ. – 2010. – № 4. – C. 72 – 74.uk_UA
dc.subjectant colony systemuk_UA
dc.subjecttraveling salesman problemuk_UA
dc.subjectant algorithmsuk_UA
dc.subjectoptimization traveling salesman routeuk_UA
dc.subjecttaboo-listuk_UA
dc.subjectvisibility and virtual pheromone trailuk_UA
dc.titleThe use of ant colony optimization algorithms for the problem of optimal route searchuk_UA
dc.typeConference Abstractuk_UA

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