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JCR Impact Factor: 1.102
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Issues per year: 4
Current issue: May 2020
Next issue: Aug 2020
Avg review time: 74 days


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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2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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  3/2014 - 7

 HIGHLY CITED PAPER 

Improvement of the Gravitational Search Algorithm by means of Low-Discrepancy Sobol Quasi Random-Number Sequence Based Initialization

ALTINOZ, O. T. See more information about ALTINOZ, O. T. on SCOPUS See more information about ALTINOZ, O. T. on IEEExplore See more information about ALTINOZ, O. T. on Web of Science, YILMAZ, A. E. See more information about  YILMAZ, A. E. on SCOPUS See more information about  YILMAZ, A. E. on SCOPUS See more information about YILMAZ, A. E. on Web of Science, WEBER, G.-W. See more information about WEBER, G.-W. on SCOPUS See more information about WEBER, G.-W. on SCOPUS See more information about WEBER, G.-W. 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 (764 KB) | Citation | Downloads: 364 | Views: 3,446

Author keywords
evolutionary computation, random number generation, Sobol quasi random number generation, gravitational search algorithm

References keywords
algorithm(9), swarm(5), search(5), optimization(5), gravitational(4), genetic(4), evolutionary(4), computation(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 55 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03007
Web of Science Accession Number: 000340869800007
SCOPUS ID: 84907331643

Abstract
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Nature-inspired optimization algorithms can obtain the optima by updating the position of each member in the population. At the beginning of the algorithm, the particles of the population are spread into the search space. The initial distribution of particles corresponds to the beginning points of the search process. Hence, the aim is to alter the position for each particle beginning with this initial position until the optimum solution will be found with respect to the pre-determined conditions like maximum iteration, and specific error value for the fitness function. Therefore, initial positions of the population have a direct effect on both accuracy of the optima and the computational cost. If any member in the population is close enough to the optima, this eases the achievement of the exact solution. On the contrary, individuals grouped far away from the optima might yield pointless efforts. In this study, low-discrepancy quasi-random number sequence is preferred for the localization of the population at the initialization phase. By this way, the population is distributed into the search space in a more uniform manner at the initialization phase. The technique is applied to the Gravitational Search Algorithm and compared via the performance on benchmark function solutions.


References | Cited By

Cited-By ISI Web of Science

Web of Science® Times Cited: 3 [View]
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Cited-By CrossRef

SCOPUS® Times Cited: 4
View record in SCOPUS®
[Free preview]

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Cited-By CrossRef

[1] Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm, Ebrahimi Mood, Sepehr, Javidi, Mohammad Masoud, Evolving Systems, ISSN 1868-6478, 2019.
Digital Object Identifier: 10.1007/s12530-019-09264-x
[CrossRef]

[2] Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters, Pelusi, Danilo, Mascella, Raffaele, Tallini, Luca, Nayak, Janmenjoy, Naik, Bighnaraj, Abraham, Ajith, Expert Systems with Applications, ISSN 0957-4174, Issue , 2018.
Digital Object Identifier: 10.1016/j.eswa.2018.02.026
[CrossRef]

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


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