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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: 644266260
doi: 10.4316/AECE


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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  2/2015 - 7

Incorporating the Avoidance Behavior to the Standard Particle Swarm Optimization 2011

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, DUCA, A. See more information about  DUCA, A. on SCOPUS See more information about  DUCA, A. on SCOPUS See more information about DUCA, A. on Web of Science, CIUPRINA, G. See more information about CIUPRINA, G. on SCOPUS See more information about CIUPRINA, G. on SCOPUS See more information about CIUPRINA, G. on Web of Science
 
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Download PDF pdficon (785 KB) | Citation | Downloads: 178 | Views: 899

Author keywords
particle swarm optimization, social factors, cognitive informatics, performance evaluation

References keywords
swarm(14), optimization(14), systems(5), evolutionary(5), computation(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 51 - 58
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02007
Web of Science Accession Number: 000356808900007
SCOPUS ID: 84979834398

Abstract
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Inspired from social and cognitive behaviors of animals living as swarms; particle swarm optimization (PSO) provides a simple but very powerful tool for researchers who are dealing with collective intelligence. The algorithm depends on modeling the very basic random behavior (i.e. exploration capability) of individuals in addition to their tendency to revisit positions of good memories (cognitive behavior) and tendency to keep an eye on and follow the majority of swarm members (social behavior). The balance among these three major behaviors is the key of success of the algorithm. On the other hand, there are other social and cognitive phenomena, which might be useful for improvement of the algorithm. In this paper, we particularly investigate avoidance from the bad behavior. We propose modifications about modeling the Standard PSO 2011 formulation, and we test performance of our proposals at each step via benchmark functions, and compare the results of the proposed algorithms with well-known algorithms. Our results show that incorporation of Social Avoidance behavior into SPSO11 improves the performance. It is also shown that in case the Social Avoidance behavior is applied in an adaptive manner at the very first iterations of the algorithm, there might be further improvements.


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

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[2] M. Dorigo, "Optimization, learning and natural algorithms (in Italian)," Ph.D. Thesis, Politecnico di Milano, Italy, 1992.

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[CrossRef] [Web of Science Times Cited 1153]


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[CrossRef] [Web of Science Times Cited 16694] [SCOPUS Record]


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[CrossRef] [SCOPUS Times Cited 142]


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[CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 171]


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[CrossRef]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 835]


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[CrossRef] [Web of Science Times Cited 837] [SCOPUS Times Cited 1239]


[12] P. Poli, "Analysis of the publications on the applications of particle swarm optimization," Journal of Artificial Evolution and Applications, Article ID: 685175, 2008.
[CrossRef]


[13] M. Zambrano-Bigiarini, M. Clerc, R. Rojas, "Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements," IEEE Congress on Evolutionary Computation (CEC), pp. 2337 - 2344, 2013.
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[15] G. Ciuprina, D. Ioan, I. Munteanu, "Use of intelligent-particle swarm optimization in electromagnetics," IEEE Transactions on Magnetics, vol. 38, no. 2, pp. 1037-1040, 2002.
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 217]


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[CrossRef]


[17] A. Biswas, A. Kumar, K.K. Mishra, "Particle Swarm Optimization with cognitive avoidance component," International Conf. on Advances in Computer, Communication and Informatics, pp. 149 - 154, 2013.
[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Times Cited 32]




References Weight

Web of Science® Citations for all references: 18,956 TCR
SCOPUS® Citations for all references: 2,757 TCR

Web of Science® Average Citations per reference: 903 ACR
SCOPUS® Average Citations per reference: 131 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 2016-12-06 15:22 in 90 seconds.




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


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