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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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  2/2015 - 7

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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: 711 | Views: 3,091

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.

[3] M. Dorigo, G. DiCaro and L.M. Gambardella, "Ant algorithms for discrete optimization," Artificial Life, vol. 5, no. 2, pp. 137-172, 1999.
[CrossRef] [Web of Science Times Cited 1672] [SCOPUS Times Cited 2240]


[4] J. Kennedy, R. Eberhart, "Particle swarm optimization," IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
[CrossRef] [Web of Science Times Cited 31651]


[5] G. Bilchev, I. C. Parmee, "The ant colony metaphor for searching continuous design spaces," AISB Workshop on Evolutionary Computation, vol. 993, pp. 25-39, 1995.
[CrossRef] [SCOPUS Times Cited 286]


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[CrossRef] [Web of Science Times Cited 164] [SCOPUS Times Cited 219]


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[CrossRef] [SCOPUS Times Cited 127]


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[CrossRef] [Web of Science Times Cited 3360]


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[CrossRef] [SCOPUS Times Cited 1705]


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[11] J. Robinson, Y. Rahmat-Samii, "Particle swarm optimization in electromagnetics," IEEE Transactions on Antennas and Propagation vol. 52, no. 2, pp. 397-407, 2004.
[CrossRef] [Web of Science Times Cited 1561] [SCOPUS Times Cited 2059]


[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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[14] C. Yang, D. Simon, "A new particle swarm optimization technique," 18th International Conference on Systems Engineering, pp. 164-169, 2005.
[CrossRef] [SCOPUS Times Cited 104]


[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 224] [SCOPUS Times Cited 301]


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[CrossRef] [SCOPUS Times Cited 2]


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[CrossRef] [SCOPUS Times Cited 11]


[18] J.J. Liang, P.N. Suganthan and K. Deb, "Novel Composition Test Functions for Numerical Global Optimization," IEEE Congress on Evolutionary Computation, pp. 68-75, 2005. Matlab codes of benchmark problems: http://www.ntu.edu.sg/home/epnsugan/ index_files/SIS2005-function-codes.zip

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[CrossRef] [SCOPUS Times Cited 57]




References Weight

Web of Science® Citations for all references: 38,632 TCR
SCOPUS® Citations for all references: 7,471 TCR

Web of Science® Average Citations per reference: 1,840 ACR
SCOPUS® Average Citations per reference: 356 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 2024-05-20 12:36 in 108 seconds.




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