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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: 643243560
doi: 10.4316/AECE


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 HIGH-IMPACT PAPER 

Golden Sine Algorithm: A Novel Math-Inspired Algorithm

TANYILDIZI, E. See more information about TANYILDIZI, E. on SCOPUS See more information about TANYILDIZI, E. on IEEExplore See more information about TANYILDIZI, E. on Web of Science, DEMIR, G. See more information about DEMIR, G. on SCOPUS See more information about DEMIR, G. on SCOPUS See more information about DEMIR, G. on Web of Science
 
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Download PDF pdficon (1,408 KB) | Citation | Downloads: 819 | Views: 2,190

Author keywords
artificial intelligence, computational intelligence, evolutionary computation, heuristic algorithms, optimization

References keywords
optimization(24), algorithm(14), algorithms(7), inspired(5), computation(5), yang(4), swarm(4), software(4), jadvengsoft(4), evolutionary(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02010
Web of Science Accession Number: 000405378100010
SCOPUS ID: 85020089767

Abstract
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In this study, Golden Sine Algorithm (Gold-SA) is presented as a new metaheuristic method for solving optimization problems. Gold-SA has been developed as a new search algorithm based on population. This math-based algorithm is inspired by sine that is a trigonometric function. In the algorithm, random individuals are created as many as the number of search agents with uniform distribution for each dimension. The Gold-SA operator searches to achieve a better solution in each iteration by trying to bring the current situation closer to the target value. The solution space is narrowed by the golden section so that the areas that are supposed to give only good results are scanned instead of the whole solution space scan. In the tests performed, it is seen that Gold-SA has better results than other population based methods. In addition, Gold-SA has fewer algorithm-dependent parameters and operators than other metaheuristic methods, increasing the importance of this method by providing faster convergence of this new method.


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

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References Weight

Web of Science® Citations for all references: 45,136 TCR
SCOPUS® Citations for all references: 25,888 TCR

Web of Science® Average Citations per reference: 1,328 ACR
SCOPUS® Average Citations per reference: 761 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 2020-08-05 07:11 in 167 seconds.




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