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

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


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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
 
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Download PDF pdficon (764 KB) | Citation | Downloads: 447 | Views: 3,896

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  «-- Click to see who has cited this paper

[1] Y. W. Leung, Y. Wang, Y. W. Leung, "An orthogonal genetic algorithm with quantization for global numerical optimization," IEEE Transactions on Evolutionary Computation, Vol. 5, No. 1, pp. 41-53, 2001.
[CrossRef] [Web of Science Times Cited 551] [SCOPUS Times Cited 760]


[2] O. T. Altinoz, A. E. Yilmaz, G. W. Weber, "Application of chaos embedded PSO for PID tuning," International Journal of Computers, Communications and Control, Vol. 7, No. 2, pp. 204-218, 2012.
[CrossRef] [SCOPUS Times Cited 13]


[3] E. Masahian, D. Sedighizadeh, "Multiobjective particle swarm optimization and NPSO-based algorithms for robot path planning," Advances in Electrical and Computer Engineering, Vol. 10, No. 4, pp. 69-76, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 43] [SCOPUS Times Cited 53]


[4] A. Ratnaweera, S. K. Halgamuge, H.C. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 240-255, 2004.
[CrossRef] [Web of Science Times Cited 1888] [SCOPUS Times Cited 2441]


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[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[6] G. Mortinovic, D. Bojer, "Elitist ant system with 2-opt local search for the traveling salesman problem," Advances in Electrical and Computer Engineering, Vol. 12, No. 1, pp. 25-32, 2012.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 5]


[7] J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 281-295, 2006.
[CrossRef] [Web of Science Times Cited 2168] [SCOPUS Times Cited 2772]


[8] O. T. Altinoz, A. E. Yilmaz, "Particle swarm optimization with parameter dependency walls and its sample application to the microstrip-like interconnect line design," AEÜ-International Journal of Electronics and Communications, Vol. 66, No. 2, pp. 107-114, 2012.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]


[9] O. Brudaru, D. Popovich, C. Copecanu, "Cellular genetic algorithm with communicating grids for assembly line balancing problems," Advances in Electrical and Computer Engineering, Vol. 10, No. 2, pp. 87-93, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[10] B. Liu, L. Wang, Y.H. Jin, "An effective PSO-based memetic algorithm for flow shop scheduling," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 37, No. 1, pp. 18-27, 2007.
[CrossRef] [Web of Science Times Cited 320] [SCOPUS Times Cited 390]


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[12] E. Rashedi, H. Nezamabadi, S. Saryazdi, "GSA: A gravitational search algorithm," Information Sciences, Vol. 179, No. 13, pp. 2232-2248, 2009.
[CrossRef] [Web of Science Times Cited 3236] [SCOPUS Times Cited 3964]


[13] E. Rashedi, H. Nezamabadi, S. Saryazdi, "BGSA: Binary gravitational search algorithm," Natural Computing, Vol. 9, No. 3, pp. 727-745, 2010.
[CrossRef] [Web of Science Times Cited 406] [SCOPUS Times Cited 507]


[14] P. Bradley, B.L. Fox, "Algorithm 659: Implementing Sobol’s quasirandom sequence generator," ACM Transactions on Mathematical Software, Vol. 14, No. 1, pp. 88-100, 1988.
[CrossRef] [Web of Science Times Cited 471] [SCOPUS Times Cited 555]


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[16] O. T. Altinoz, A. E. Yilmaz, G. W. Weber, "Orthogonal array based performance improvement in the gravitational search algorithm," Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 21, No. 1, pp. 174-185, 2013.

[17] H. Maaranen, K. Miettinen, M. M. Makela, "Quasi-random initial population for genetic algorithms," Computers & Mathematics with Applications, Vol. 47, No. 12, pp. 1885-1895, 2004.
[CrossRef] [Web of Science Times Cited 99]


[18] S. A. Kazarlis, A.G. Bakirtzis, V. A. Petridis, "A genetic algorithm solution to the unit commitment problem," IEEE Transactions on Power Systems, Vol. 11, No. 1, pp. 83-92, 1996.
[CrossRef] [Web of Science Times Cited 751] [SCOPUS Times Cited 1072]


[19] E. Rashedi, H. Nezamabadi, S. Saryazdi, "Filter modeling using gravitational search algorithm," Engineering Applications of Artificial Intelligence, Vol. 24, No. 1, pp. 117-122, 2011.
[CrossRef] [Web of Science Times Cited 235] [SCOPUS Times Cited 305]


[20] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization," 2005 IEEE Congress on Evolutionary Computation (CEC 2005), pp. 1-5, 2005.



References Weight

Web of Science® Citations for all references: 10,186 TCR
SCOPUS® Citations for all references: 12,855 TCR

Web of Science® Average Citations per reference: 485 ACR
SCOPUS® Average Citations per reference: 612 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 2021-11-27 11:55 in 111 seconds.




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


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