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JCR Impact Factor: 0.700
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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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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  2/2015 - 7

 HIGH-IMPACT PAPER 

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: 870 | Views: 3,502

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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 10
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Cited-By CrossRef

[1] A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries, Lai, Xin, Qin, Chao, Gao, Wenkai, Zheng, Yuejiu, Yi, Wei, Applied Sciences, ISSN 2076-3417, Issue 9, Volume 8, 2018.
Digital Object Identifier: 10.3390/app8091592
[CrossRef]

[2] Energy Efficient Control of High Speed IPMSM Drives - A Generalized PSO Approach, GECIC, M., KAPETINA, M., MARCETIC, D., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 16, 2016.
Digital Object Identifier: 10.4316/AECE.2016.01004
[CrossRef] [Full text]

[3] QPSO with avoidance behaviour to solve electromagnetic optimization problems, Duca, Anton, Duca, Laurentiu, Ciuprina, Gabriela, International Journal of Applied Electromagnetics and Mechanics, ISSN 1383-5416, Issue 1, Volume 59, 2019.
Digital Object Identifier: 10.3233/JAE-171170
[CrossRef]

[4] Multi-Objective QPSO Algorithms to Solve an Electromagnetic Benchmark Problem, Mamoc, Cristina, Duca, Anton, Ciuprina, Gabriela, Lup, Aurel-Sorin, 2020 International Symposium on Fundamentals of Electrical Engineering (ISFEE), ISBN 978-1-7281-9038-9, 2020.
Digital Object Identifier: 10.1109/ISFEE51261.2020.9756185
[CrossRef]

[5] Quantum-behaved PSO with neighborhood strategies for solving NDET inverse problems, Duca, Laurentiu-Cristian, Ioan, Daniel, Popescu, Cornel, 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), ISBN 978-1-5090-5160-1, 2017.
Digital Object Identifier: 10.1109/ATEE.2017.7905178
[CrossRef]

[6] Multiphase System of Coils as Illustrated by Magnetotherapy, Syrek, Przemyslaw, Skowron, Mikolaj, Ciesla, Antoni, 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE), ISBN 978-1-7281-0101-9, 2019.
Digital Object Identifier: 10.1109/ATEE.2019.8724931
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
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