|2/2017 - 10|
Golden Sine Algorithm: A Novel Math-Inspired AlgorithmTANYILDIZI, E. , DEMIR, G.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,408 KB) | Citation | Downloads: 708 | Views: 1,863|
artificial intelligence, computational intelligence, evolutionary computation, heuristic algorithms, optimization
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
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|
| S. Mirjalili, S. M. Mirjalili, A. Lewis, "Grey wolf optimizer", Advances in Engineering Software, vol. 69, pp. 46-61, 2014. |
[CrossRef] [Web of Science Times Cited 1693] [SCOPUS Times Cited 2361]
 G. Demir, B. Alatas, "Lig sampiyonasi algoritmasi ile gezgin satici probleminin çözümü", 1st International Conference on Engineering Technology and Applied Sciences (ICETAS), Afyon, Turkey, pp. 793-800, 2016.
 A. Prakasam, N. Savarimuthu, "Metaheuristic algorithms and polynomial turing reductions: a case study based on ant colony optimization", Procedia Computer Science, vol. 46, pp. 388-395, 2015.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]
 I. Fister Jr., X. S. Yang, D. Fister, I. Fister, "A brief review of nature-inspired algorithms for optimization", Elektrotehniski Vestnik, vol. 80, no. 3, pp. 1-7, 2013.
 J. H. Holland, "Genetic algorithms", Scientific American, vol. 267, pp. 66-72, 1992.
[CrossRef] [Web of Science Times Cited 1064] [SCOPUS Times Cited 1619]
 D. Simon, "Biogeography-based optimization", Evolutionary Computation, IEEE Transactions on, vol. 12, no. 6, pp. 702-713, 2008.
[CrossRef] [Web of Science Times Cited 1386] [SCOPUS Times Cited 1821]
 Y. Shi, "An optimization algorithm based on brainstorming process", International Journal of Swarm Intelligence Research (IJSIR), vol. 2, no.4, pp. 35-62, 2011.
 A. Kaveh and N. Farhoudi, "A new optimization method: Dolphin echolocation", Advances in Engineering Software, vol. 59, pp. 53-70, 2013.
[CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 193]
 X. S. Yang, "Flower pollination algorithm for global optimization", Unconventional Computation and Natural Computation, pp. 240-249, 2012.
[CrossRef] [SCOPUS Times Cited 780]
 J. Kennedy, R. Eberhart, "Particle swarm optimization", in Neural Networks, Proceedings, IEEE International Conference on, vol. 4, pp. 19421948, IEEE, 1995.
[CrossRef] [Web of Science Times Cited 24902]
 M. Dorigo, "Optimization, learning and natural algorithms", Ph. D. Thesis, Politecnico di Milano, Italy, 1992.
 K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control", Control Systems, IEEE, vol. 22, no. 3, pp. 52-67, 2002.
[CrossRef] [Web of Science Times Cited 1455] [SCOPUS Times Cited 2043]
 X. S. Yang, "A new metaheuristic bat-inspired algorithm", Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, pp. 65-74, 2010.
[CrossRef] [SCOPUS Times Cited 2075]
 X. S. Yang, "Firefly algorithm, stochastic test functions and design optimization", International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78-84, 2010.
[CrossRef] [Web of Science Times Cited 846] [SCOPUS Times Cited 1129]
 S. Mirjalili, "The ant lion optimizer", Advances Engineering Software, vol. 83, pp. 80-8, 2015.
[CrossRef] [Web of Science Times Cited 529] [SCOPUS Times Cited 696]
 S. Mirjalili, S. M. Mirjalili, "The whale optimization algorithm", Advances Engineering Software, vol. 95, pp. 51-67, 2016.
[CrossRef] [Web of Science Times Cited 856] [SCOPUS Times Cited 1208]
 A. Hatamlou, "Black hole: A new heuristic optimization approach for data clustering", Information Sciences, vol. 222, pp. 175-184, 2013.
[CrossRef] [Web of Science Times Cited 295] [SCOPUS Times Cited 380]
 A. Kaveh, S. Talatahari, "A novel heuristic optimization method: charged system search", Acta Mechanica, vol. 213, no. 3, pp. 267-289, 2010.
[CrossRef] [Web of Science Times Cited 477] [SCOPUS Times Cited 569]
 E. Cuevas, D. Oliva, D. Zaldivar, M. Perez, R. Rojas, "Circle detection algorithm based on electromagnetism-like optimization", vol. 38, pp. 907-934, 2013.
[CrossRef] [SCOPUS Times Cited 3]
 E. Rashedi, H. N. Pour, S. Saryazdi, "GSA: a gravitational search algorithm. Information sciences", vol. 179, no. 13, pp. 2232-2248, 2009.
[CrossRef] [Web of Science Times Cited 2119] [SCOPUS Times Cited 2776]
 Z. W. Geem, J. H. Kim, G. V. Loganathan, "A new heuristic optimization algorithm: harmony search", Simulation, vol. 76, no. 2, pp. 60-68, 2001.
 H. Shayeghi, J. Dadashpour, "Anarchic society optimization based pid control of an automatic voltage regulator (avr) system", Electrical and Electronic Engineering, vol. 2, no. 4, pp. 199-207, 2012.
 E. A. Gargari, C. Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition", Evolutionary Computation, 2007, CEC 2007, IEEE Congress on, pp. 4661-4667, IEEE, 2007.
[CrossRef] [Web of Science Times Cited 943] [SCOPUS Times Cited 1281]
 F. Ramezani, S. Lotfi, "Social-based algorithm", Applied Soft Computing, vol. 13, pp. 2837-2856, 2013.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 37]
 S. A. Salem, "BOA: A novel optimization algorithm", International Conference on Engineering and Technology (ICET), pp. 1-5, Egypt, IEEE, 2012.
[CrossRef] [SCOPUS Times Cited 11]
 S. Mirjalili, "SCA: A Sine Cosine Algorithm for solving optimization problems", Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
[CrossRef] [Web of Science Times Cited 360] [SCOPUS Times Cited 554]
 F. Altunbey, B. Alatas, "Sosyal ag analizi için sosyal tabanli yapay zeka optimizasyon algoritmalarinin incelenmesi", Int. J. Pure Appl. Sci., vol. 1, pp. 33-52, 2015.
 R. K. Arora. Optimization Algorithms and Applications. ISBN-13: 978-1-4987-2115-8. pp. 46-47, 2015.
 P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. Chen, A. Auger, "Problem definitions and evaluation criteria for the CEC 2005 special session on realparameter optimization" KanGAL report, vol. 2005005, 2005.
 J. Derrac, S. García, D. Molina, F. Herrera, "A practical tutorial on the use of non-parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms", Swarm Evol. Comput., vol.1, no.1, pp. 3-18, 2011.
[CrossRef] [Web of Science Times Cited 1533] [SCOPUS Times Cited 1768]
 C. A. C. Coello, "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art". Comput Methods Appl Mech Eng, vol. 191, no.11-12, pp. 1245-1287, 2002.
[CrossRef] [Web of Science Times Cited 1124] [SCOPUS Times Cited 1362]
 S. H. Nasseri, Z. Alizadeh, F. Taleshian, "Optimized solution of pressure vessel design using geometric programming", The Journal of Mathematics and Computer Science, vol. 4, no. 3, pp. 344 349, 2012.
 M. Li, H. Zhao, X. Weng, T. Han, "Cognitive behavior optimization algorithm for solving optimization problems", Applied Soft Computing, vol. 39, pp. 199 222, 2016.
Web of Science® Citations for all references: 39,758 TCR
SCOPUS® Citations for all references: 22,670 TCR
Web of Science® Average Citations per reference: 1,169 ACR
SCOPUS® Average Citations per reference: 667 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 2020-02-25 00:53 in 165 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
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.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.