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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2012 - 5

Elitist Ant System with 2-opt Local Search for the Traveling Salesman Problem

MARTINOVIC, G. See more information about MARTINOVIC, G. on SCOPUS See more information about MARTINOVIC, G. on IEEExplore See more information about MARTINOVIC, G. on Web of Science, BAJER, D. See more information about BAJER, D. on SCOPUS See more information about BAJER, D. on SCOPUS See more information about BAJER, D. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,384 KB) | Citation | Downloads: 1,145 | Views: 3,706

Author keywords
2-opt algorithm, elitist ant system, local search, Traveling Salesman Problem, search stagnation

References keywords
optimization(10), problem(7), colony(7), traveling(6), salesman(6), algorithm(6), stutzle(4), local(4), dorigo(4), computational(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-02-28
Volume 12, Issue 1, Year 2012, On page(s): 25 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.01005
Web of Science Accession Number: 000301075000005
SCOPUS ID: 84860778058

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The Traveling Salesman Problem is one of the most famous problems in combinatorial optimization. The paper presents an algorithm based upon the elitist ant system for solving the traveling salesman problem. 2-opt local search is incorporated in the elitist ant system, and it is used for improvement of a given number of solutions previously constructed by artificial ants. A simple mechanism for avoiding a too early stagnation of the search is also proposed. The aforementioned is based on depositing strong pheromones on solution edges of randomly selected ants called random elitist ants. The aim is to encourage exploration in a greater area of the solution space. Experimental analysis shows how high-quality solutions can be achieved by using the considered algorithm instead of the usual elitist ant system with incorporated 2-opt local search.

References | Cited By  «-- Click to see who has cited this paper

[1] D. L. Applegate, R. E. Bixby, V. Chvatal and W. J. Cook. The Traveling Salesman Problem: A computational study. Princeton University Press, 2006.

[2] S. Consoli, K. Darby-Dowman, "Combinatorial optimization and metaheuristics", Annals of Operations Research, vol. 140, no. 1, pp. 189-213, 2007. [Handle]

[3] Metaheuristics Network, Project Summary, [Online] Available: Temporary on-line reference link removed - see the PDF document

[4] D. S. Johnson, L. A. McGeoch, "The Traveling Salesman Problem: A case study in local optimization", in: E. H. L. Aarts, J. K. Lenstra." Local Search in Combinatorial Optimization". John Wiley and Sons, 1997, pp. 215-310.

[5] F. Greco (Ed.). Traveling Salesman Problem. In-Tech, 2008.

[6] M. Yoshikawa, T. Nagura, "Adaptive Ant Colony Optimization considering intensification and diversification", in Proc. of Int. MultiConf. of Engineers and Computer Scientists, Vol. 1, Hong Kong, 2009, pp. 200-203.

[7] L. Wong, M. Y-H. Low, C. S. Chong, "Bee Colony Optimization with local search for Traveling Salesman Problem", Int. J. on Artificial Intelligence Tools, vol. 19, no. 3, pp. 305-334, Jun. 2010.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 32]

[8] H. Shah-Hosseini, "The Intelligent Water Drops algorithm: A nature-inspired swarm-based optimization algorithm", Int. J. Bio-Inspired Computation, vol. 1, no. 1/2, pp. 71-79, 2009.
[CrossRef] [SCOPUS Times Cited 222]

[9] M. Djordjevic, M. Tuba, B. Djordjevic, "Impact of grafting a 2-opt algorithm based local searcher into the genetic algorithm", in Proc. of 9th WSEAS Int. Conf. on Applied Informatics and Communications, Moscow, pp. 485-490.

[10] G. A. Jayalakshmi, S. Sathiamoorthy, R. Rajaram, "A hybrid genetic algorithm - A new approach to solve Traveling Salesman Problem", Int. J. of Computational Engineering Science, vol. 2, no. 2, pp. 339-355, Jun. 2001.

[11] G. Martinovic, I. Aleksi, A. Baumgartner, "Single-Commodity Vehicle Routing Problem with Pickup and Delivery Service", Mathematical Problems in Engineering, vol. 2008, Art. no. 697981, pp. 1-17, 2008.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 21]

[12] M. Dorigo, T. Stutzle. Ant Colony Optimization. MIT Press, 2004.

[13] O. Cordon, F. Herrera, T. Stutzle, "A review on the Ant Colony Optimization metaheuristic: Basis, models and new trends", Mathware & Soft Computing, vol. 9, no. 3, pp. 141-175, 2002.

[14] M. Dorigo, M. Birattari, T. Stutzle, "Ant Colony Optimization -Artificial ants as a computational intelligence technique", IEEE Computational Intelligence Mag., vol. 1, no. 4, pp. 28-39, 2006.

[15] M. Dorigo, V. Maniezzo, A. Colorni, "Ant System: optimization by a colony of cooperating agents", IEEE Trans. on Systems, Man and Cybernetics-Part B, vol. 26, no. 1, pp. 29-41, Feb. 1996.
[CrossRef] [Web of Science Times Cited 5235] [SCOPUS Times Cited 7326]

[16] M. Englert, H. Rölin, B. Vöcking, "Worst case and probabilistic analysis of the 2-opt algorithm for the TSP", in Proc. of 18th Annu. ACM-SIAM Symposium on Discrete Algorithms, New Orleans, 2007, pp. 1295-1304.

[17] T. Stutzle, H. H. Hoos, "MAX-MIN Ant System", Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, Jun. 2000.

[18] M. Dorigo, L. M. Gambardella, "Ant Colony System: A cooperative learning approach to the Traveling Salesman Problem", IEEE Trans. on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, Apr. 1997.
[CrossRef] [SCOPUS Times Cited 5185]

[19] Ruprecht-Karls-Universität Heidelberg, TSPLIB, [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 5,274 TCR
SCOPUS® Citations for all references: 12,786 TCR

Web of Science® Average Citations per reference: 278 ACR
SCOPUS® Average Citations per reference: 673 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-14 22:05 in 37 seconds.

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