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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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  2/2013 - 12

System Optimization Using a Parallel Stochastic Approach

ZAPLATILEK, K. See more information about ZAPLATILEK, K. on SCOPUS See more information about ZAPLATILEK, K. on IEEExplore See more information about ZAPLATILEK, K. on Web of Science, LEUCHTER, J. See more information about LEUCHTER, J. on SCOPUS See more information about LEUCHTER, J. on SCOPUS See more information about LEUCHTER, J. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (525 KB) | Citation | Downloads: 375 | Views: 2,172

Author keywords
stochastic optimization, parallel approach, Matlab

References keywords
optimization(12), group(4), delay(4), algorithm(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-05-31
Volume 13, Issue 2, Year 2013, On page(s): 73 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.02012
Web of Science Accession Number: 000322179400012
SCOPUS ID: 84878912821

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This paper describes an original stochastic algorithm based on a parallel approach. The algorithm is suitable especially for a real technical system optimization. A few independent pseudorandom generators are used. They generate independent variable vectors along all of the optimized system axes. Local optimal values are used to define a final pseudorandom generator with a narrower interval around the global optimum. Theoretical foundations are introduced and a few practical experiments are presented. The described method is also suitable for the quality classification of the pseudorandom generators using the selected RGB color scheme. Main advantages of this approach are discussed. The algorithm was developed in the MATLAB environment.

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

[1] S. S. Rao, Engineering Optimization. Theory and Practice. John Wiley&Sons, Inc., 2009.

[2] R. Baldick, Applied optimization. Cambridge university Press, 2006.
[CrossRef] [SCOPUS Times Cited 108]

[3] P. Venkataraman, Applied optimization with MATLAB programming, John Wiley&Sons, Inc., 2009.

[4] U. Diwekar, Introduction to Applied Optimization. Springer, 2008.

[5] K. Zaplatilek, "Analogue filter group delay optimization using a stochastic approach," International Journal of Numerical Modelling, 2010, vol. 23, no. 3, pp. 215-230.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]

[6] R. Storn, "Differential Evolution Design of an IIR-Filter with Requirements for Magnitude and Group Delay," Technical Report TR-95-026, ICSI, May 1995.

[7] K. Deb, A. Anand, D. Joshi, "A computationally efficient evolutionary algorithm for real parameter optimization," Evolutionary Computation Journal, 2002, vol. 10, no. 4, pp. 371-395.
[CrossRef] [Web of Science Times Cited 373] [SCOPUS Times Cited 446]

[8] P. Ziska, M. Laipert, "Analog Group Delay Equalizers Design based on Evolutionary Algorithm," Radioengineering, vol. 15, no. 1, 2006, pp. 1-5.

[9] R. Chelouah, P.A. Siarry, "A continuous genetic algorithm designed for the global optimization of multimodal functions," Journal of Heuristic, 2000, vol. 6, no. 2, pp. 191-213.
[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 218]

[10] Q. Zhang, J. Sun, E. Tang, J.A. Ford, "Hybrid estimation of distribution algorithm for global optimization," Engineering Computations, 2003, vol. 21, no. 1, pp. 91-107.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 73]

[11] K. Zaplatilek, M. Talpa, J. Leuchter, "Optimization Algorithms Testing and Convergence by Using a Stacked Histogram," Advances in Electrical and Computer Engineering, 2011, vol. 11, no. 1. pp. 11-16.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]

[12] K. Zaplatilek, P. Ziska, K. Hajek, "Practice Utilization of Algorithms for Analog Filter Group Delay Optimization," Radioengineering, 2007, vol. 16, no. 1, pp. 7-15.

[13] J. H. Mathews, K. K. Fink, Numerical Methods Using MATLAB. Prentice-Hall, Inc., 2004.

[14] R. Shaumann, M. S. Ghausi, K. R. Laker, Design of Analog Filters. Prentice Hall, New Jersey, 1990.

[15] M. J. D. Powell, S. Scholtes, "Modelling and Optimization: Methods, Theory and Applications," in Proc. 19th IFIP TC7 Conference on System Modelling and Optimization, Cambridge, 1999.

[16] [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 614 TCR
SCOPUS® Citations for all references: 851 TCR

Web of Science® Average Citations per reference: 36 ACR
SCOPUS® Average Citations per reference: 50 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 2020-11-25 12:48 in 48 seconds.

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