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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: 75 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
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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  2/2010 - 15

 HIGHLY CITED PAPER 

Cellular Genetic Algorithm with Communicating Grids for Assembly Line Balancing Problems

BRUDARU, O. See more information about BRUDARU, O. on SCOPUS See more information about BRUDARU, O. on IEEExplore See more information about BRUDARU, O. on Web of Science, POPOVICI, D. See more information about  POPOVICI, D. on SCOPUS See more information about  POPOVICI, D. on SCOPUS See more information about POPOVICI, D. on Web of Science, COPACEANU, C. See more information about COPACEANU, C. on SCOPUS See more information about COPACEANU, C. on SCOPUS See more information about COPACEANU, C. on Web of Science
 
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Download PDF pdficon (827 KB) | Citation | Downloads: 1,445 | Views: 5,556

Author keywords
cellular genetic algorithms, communicating grids, I/U-shaped assembly lines, parallel workstations, and task compatibility constraints

References keywords
link(7), genetic(6), balancing(5), assembly(5), algorithms(5), line(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 87 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02015
Web of Science Accession Number: 000280312600015
SCOPUS ID: 77954656030

Abstract
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This paper presents a new approach with cellular multigrid genetic algorithms for the I-shaped and U-shaped assembly line balancing problems, including parallel workstations and compatibility constraints. First, a cellular hybrid genetic algorithm that uses a single grid is described. Appropriate operators for mutation, hypermutation, and crossover and two devoration techniques are proposed for creating and maintaining groups based on similarity. This monogrid algorithm is extended for handling many populations placed on different grids. In the multigrid version, the population of each grid is organized in clusters using the positional information of the chromosomes. A similarity preserving communication protocol between the clusters placed on different grids is introduced. The experimental evaluation shows that the multigrid cellular genetic algorithm with communicating grids is better than the hybrid genetic algorithm used for building it, whereas it dominates the monogrid version in all cases. Absolute performance is evaluated using classical benchmarks. The role of certain components of the cellular algorithm is explained and the effect of some parameters is evaluated.


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

[1] Ajenblit, D. A., Wainwright, R. L., "Applying genetic algorithms to the U-shaped assembly line balancing problem", In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, pp. 96-101, 1998.
[CrossRef] [Web of Science Times Cited 49]


[2] Alba, E., Troya, J. M., "An analysis synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic islands", In: IPPS/SPDP Workshops, pp. 248-256, 1999.

[3] Brudaru, O., "Assembly line balancing with compatibility constraints", Econ. Comput. Econ. Cybern. Stud. Res. 27, No.1-4, pp. 59-65, (1993).

[4] Gen, M., Cheng, R., Lin, L., "Assembly Line Balancing Models, Network Models and Optimization", Springer London, pp. 477-550, 2008.

[5] Haynes, Th., Sen, S., "Evolving behavioral strategies in predators and pray", In IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems, pp. 32-37, 1995.

[6] Li, X., Suterhand, S., A., "Cellular Genetic Algorithm Simulating Predator - Prey Interactions", Technical Report, School of Comp. Sci. and Information Technology, RMIT University Melbourne, 2006.

[7] Michalewicz, Z., "Genetic Algorithms + Data structures = Evolution Programs", Springer Verlag, Berlin 1994.

[8] Paszkowicz W., "Genetic Algorithms, a Nature-Inspired Tool: Survey of Applications in Materials Science and Related Fields", Materials and Manufacturing Processes, vol. 24, 2, Feb. 2009, pp. 174 - 197

[9] Scholl, A., "Balancing and sequencing of assembly lines". 2nd edition, Physica-Verlag, Heidelberg.

[10] Tasan S., Tunali, S., "A review of the current applications of genetic algorithms in assembly line balancing", Journal of Intelligent Manufacturing, Springer Netherlands, vol. 19, Number 1 / February, pp. 49-69, 2008.
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 139]


[11] Temporary on-line reference link removed - see the PDF document

[12] Temporary on-line reference link removed - see the PDF document

[13] Temporary on-line reference link removed - see the PDF document

[14] Temporary on-line reference link removed - see the PDF document

[15] Temporary on-line reference link removed - see the PDF document

[16] Temporary on-line reference link removed - see the PDF document

[17] Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 166 TCR
SCOPUS® Citations for all references: 139 TCR

Web of Science® Average Citations per reference: 10 ACR
SCOPUS® Average Citations per reference: 8 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 2024-04-18 17:09 in 17 seconds.




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


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