|1/2012 - 5|
Elitist Ant System with 2-opt Local Search for the Traveling Salesman ProblemMARTINOVIC, G. , BAJER, D.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,384 KB) | Citation | Downloads: 1,145 | Views: 3,706|
2-opt algorithm, elitist ant system, local search, Traveling Salesman Problem, search stagnation
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
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|
| D. L. Applegate, R. E. Bixby, V. Chvatal and W. J. Cook. The Traveling Salesman Problem: A computational study. Princeton University Press, 2006.
 S. Consoli, K. Darby-Dowman, "Combinatorial optimization and metaheuristics", Annals of Operations Research, vol. 140, no. 1, pp. 189-213, 2007. [Handle]
 Metaheuristics Network, Project Summary, [Online] Available: Temporary on-line reference link removed - see the PDF document
 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.
 F. Greco (Ed.). Traveling Salesman Problem. In-Tech, 2008.
 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.
 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]
 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]
 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.
 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.
 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]
 M. Dorigo, T. Stutzle. Ant Colony Optimization. MIT Press, 2004.
 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.
 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.
 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]
 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.
 T. Stutzle, H. H. Hoos, "MAX-MIN Ant System", Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, Jun. 2000.
 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]
 Ruprecht-Karls-Universität Heidelberg, TSPLIB, [Online] Available: Temporary on-line reference link removed - see the PDF document
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.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.