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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: 78 days
Avg accept to publ: 48 days
APC: 300 EUR


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
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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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: 1,619 | Views: 5,543

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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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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] MetrIntMeas a novel metric for measuring the intelligence of a swarm of cooperating agents, Iantovics, Laszlo Barna, Emmert-Streib, Frank, Arik, Sabri, Cognitive Systems Research, ISSN 1389-0417, Issue , 2017.
Digital Object Identifier: 10.1016/j.cogsys.2017.04.006
[CrossRef]

[2] A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem, Dutta, Joydeep, Barma, Partha Sarathi, Kar, Samarjit, De, Tanmay, International Journal of Business Analytics, ISSN 2334-4547, Issue 1, Volume 6, 2019.
Digital Object Identifier: 10.4018/IJBAN.2019010104
[CrossRef]

[3] Improvement of the Gravitational Search Algorithm by means of Low-Discrepancy Sobol Quasi Random-Number Sequence Based Initialization, ALTINOZ, O. T., YILMAZ, A. E., WEBER, G.-W., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 14, 2014.
Digital Object Identifier: 10.4316/AECE.2014.03007
[CrossRef] [Full text]

[4] Optimized Dynamic Load Balance Method Based on Ant Colony Optimization Algorithm, Shi, Yongge, Hu, Zhen, Lu, Zhiheng, 2021 IEEE 9th International Conference on Computer Science and Network Technology (ICCSNT), ISBN 978-1-6654-4364-7, 2021.
Digital Object Identifier: 10.1109/ICCSNT53786.2021.9615474
[CrossRef]

Updated 2 days, 14 hours ago

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


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