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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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  2/2015 - 3

Performance Comparison of the Differential Evolution and Particle Swarm Optimization Algorithms in Free-Space Optical Communications Systems

BASGUMUS, A. See more information about BASGUMUS, A. on SCOPUS See more information about BASGUMUS, A. on IEEExplore See more information about BASGUMUS, A. on Web of Science, NAMDAR, M. See more information about  NAMDAR, M. on SCOPUS See more information about  NAMDAR, M. on SCOPUS See more information about NAMDAR, M. on Web of Science, YILMAZ, G. See more information about  YILMAZ, G. on SCOPUS See more information about  YILMAZ, G. on SCOPUS See more information about YILMAZ, G. on Web of Science, ALTUNCU, A. See more information about ALTUNCU, A. on SCOPUS See more information about ALTUNCU, A. on SCOPUS See more information about ALTUNCU, A. on Web of Science
 
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Download PDF pdficon (664 KB) | Citation | Downloads: 429 | Views: 1,777

Author keywords
communication systems, cooperative systems, distance measurement, evolutionary computation, particle swarm optimization

References keywords
optimization(16), swarm(14), optical(7), evolution(7), communications(7), algorithm(7), space(6), free(6), systems(5), relay(4)
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): 17 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02003
Web of Science Accession Number: 000356808900003
SCOPUS ID: 84979832517

Abstract
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In this comparative study we evaluate the performance of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms in free space optical communications systems. In particular we obtain the optimal transmission distances for different relay nodes in the parallel decode-and-forward relaying schemes by optimizing the relay placements. We analyze that the cost functions are suitably minimized. Then we investigate the execution time and the stability of the DE and PSO algorithms for decode-and-forward relay-assisted free space optical links. The simulation results demonstrate that the PSO algorithm outperforms DE algorithm in terms of the minimization of the cost function. Furthermore the results indicate that we get the similar performance for the execution-time and optimization results in the DE and PSO algorithms.


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

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[CrossRef] [Web of Science Times Cited 166] [SCOPUS Times Cited 211]


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[CrossRef] [Web of Science Times Cited 40]


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[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 57]


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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef] [SCOPUS Record]


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[CrossRef] [Web of Science Times Cited 16778] [SCOPUS Record]


[15] M. Zhang, W. Zhangi, and Y. Sun, "Chaotic Co-evolutionary Algorithm Based on Differential Evolution and Particle Swarm Optimization," IEEE International Conference on Automation and Logistics, pp. 885-889, Shenyang, Aug. 2009.
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[16] I. Ahmed, S. Sadeque, and S. Pervin, "Margin Adaptive Resource Allocation for Multiuser OFDM Systems by Modified Particle Swarm Optimization and Differential Evolution," IEEE 21st International Conference on Electrical Communications and Computers, pp. 227-231, San Andres Cholula, Mar. 2011.
[CrossRef] [SCOPUS Times Cited 4]


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[CrossRef] [Web of Science Record] [SCOPUS Record]


[19] S. K. Goudos, Z. D. Zaharis, K. B. Baltzis, C. L. Hilas and, J. N. Sahalos, "A Comparative Study of Particle Swarm Optimization and Differential Evolution on Radar Absorbing Materials Design for EMC Applications," IEEE International Symposium on Electromagnetic Compatibility, pp. 1-4, Athens, Jun. 2009.
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[20] B. Basu, and G. K. Mahanti, "A Comparative Study of Modified Particle Swarm Optimization, Differential Evolution and Artificial Bees Colony Optimization in Synthesis of Circular Array," IEEE International Conference on Power, Control and Embedded Systems, pp. 1-5, Allahabad, Dec. 2010.
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[21] V. Roberge, M. Tarbouchi, and G. Labonte, "Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning," IEEE Transactions on Industrial Infirmatics, vol. 9, no. 1, pp. 132-141. Feb. 2013.
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[22] A. Deb, J. S. Roy, and B. Gupta, "Performance Comparison of Differential Evolution, Particle Swarm Optimization and Genetic Algorithm in the Design of Circularly Polarized Microstrip Antennas," IEEE Transactions on Antennas and Propagation, vol. 62, no. 8, pp. 3920-3928, May. 2014.
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[CrossRef]




References Weight

Web of Science® Citations for all references: 17,220 TCR
SCOPUS® Citations for all references: 559 TCR

Web of Science® Average Citations per reference: 718 ACR
SCOPUS® Average Citations per reference: 23 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 2016-12-10 03:37 in 118 seconds.




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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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