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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 77 days
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PUBLISHER

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

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


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
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  1/2012 - 5

 HIGHLY CITED PAPER 

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
 
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Download PDF pdficon (1,384 KB) | Citation | Downloads: 1,628 | Views: 5,575

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

Abstract
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Full text preview
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 30] [SCOPUS Times Cited 39]


[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 384]


[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 25] [SCOPUS Times Cited 33]


[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 7287] [SCOPUS Times Cited 10051]


[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 6752]


[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: 7,342 TCR
SCOPUS® Citations for all references: 17,259 TCR

Web of Science® Average Citations per reference: 386 ACR
SCOPUS® Average Citations per reference: 908 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 2024-04-20 01:21 in 33 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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