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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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  3/2014 - 7

 HIGH-IMPACT 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: 751 | Views: 4,940

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

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 9 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 12
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Cited-By CrossRef

[1] The impact of the conversion starting threshold on the estimation of the RMS value, Subotin, Marina, Mirković, Stefan, Gazivoda, Nemanja, Pejić, Dragan, Urekar, Marjan, Antić, Boris, Microprocessors and Microsystems, ISSN 0141-9331, Issue , 2022.
Digital Object Identifier: 10.1016/j.micpro.2022.104595
[CrossRef]

[2] Structural system identification and damage detection using adaptive hybrid Jaya and differential evolution algorithm with mutation pool strategy, Zhang, Guangcai, Hou, Jiale, Wan, Chunfeng, Xie, Liyu, Xue, Songtao, Structures, ISSN 2352-0124, Issue , 2022.
Digital Object Identifier: 10.1016/j.istruc.2022.10.130
[CrossRef]

[3] An improved algorithm for the estimation of the root mean square value as an optimal solution for commercial measurement equipment, Bulat, Marina, Mirković, Stefan, Gazivoda, Nemanja, Pejić, Dragan, Urekar, Marjan, Antić, Boris, Microprocessors and Microsystems, ISSN 0141-9331, Issue , 2024.
Digital Object Identifier: 10.1016/j.micpro.2024.105042
[CrossRef]

[4] Methods for Improving the Efficiency of Swarm Optimization Algorithms. A Survey, Hodashinsky, I. A., Automation and Remote Control, ISSN 0005-1179, Issue 6, Volume 82, 2021.
Digital Object Identifier: 10.1134/S0005117921060011
[CrossRef]

[5] Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm, Ebrahimi Mood, Sepehr, Javidi, Mohammad Masoud, Evolving Systems, ISSN 1868-6478, Issue 4, Volume 11, 2020.
Digital Object Identifier: 10.1007/s12530-019-09264-x
[CrossRef]

[6] A novel approach for nature‐based optimization algorithms: The threat factor approach, Toz, Metin, Toz, Güliz, Concurrency and Computation: Practice and Experience, ISSN 1532-0626, Issue 20, Volume 33, 2021.
Digital Object Identifier: 10.1002/cpe.6341
[CrossRef]

[7] A Review of the Use of Quasi-random Number Generators to Initialize the Population in Meta-heuristic Algorithms, Navarro, Mario A., Oliva, Diego, Ramos-Michel, Alfonso, Morales-Castañeda, Bernardo, Zaldívar, Daniel, Luque−Chang, Alberto, Archives of Computational Methods in Engineering, ISSN 1134-3060, Issue 7, Volume 29, 2022.
Digital Object Identifier: 10.1007/s11831-022-09759-y
[CrossRef]

[8] Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters, Pelusi, Danilo, Mascella, Raffaele, Tallini, Luca, Nayak, Janmenjoy, Naik, Bighnaraj, Abraham, Ajith, Expert Systems with Applications, ISSN 0957-4174, Issue , 2018.
Digital Object Identifier: 10.1016/j.eswa.2018.02.026
[CrossRef]

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