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Golden Sine Algorithm: A Novel Math-Inspired AlgorithmTANYILDIZI, E. , DEMIR, G.
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artificial intelligence, computational intelligence, evolutionary computation, heuristic algorithms, optimization
optimization(24), algorithm(14), algorithms(7), mirjalili(6), inspired(5), computation(5), yang(4), swarm(4), software(4), jadvengsoft(4)
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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 858] [SCOPUS Times Cited 1199]
 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 3] [SCOPUS Times Cited 3]
 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 828] [SCOPUS Times Cited 1288]
 D. Simon, "Biogeography-based optimization", Evolutionary Computation, IEEE Transactions on, vol. 12, no. 6, pp. 702-713, 2008.
[CrossRef] [Web of Science Times Cited 1086] [SCOPUS Times Cited 1483]
 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 102] [SCOPUS Times Cited 137]
 X. S. Yang, "Flower pollination algorithm for global optimization", Unconventional Computation and Natural Computation, pp. 240-249, 2012.
[CrossRef] [SCOPUS Times Cited 494]
 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 21391]
 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 1282] [SCOPUS Times Cited 1831]
 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 1484]
 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 674] [SCOPUS Times Cited 900]
 S. Mirjalili, "The ant lion optimizer", Advances Engineering Software, vol. 83, pp. 80-8, 2015.
[CrossRef] [Web of Science Times Cited 283] [SCOPUS Times Cited 371]
 S. Mirjalili, S. M. Mirjalili, "The whale optimization algorithm", Advances Engineering Software, vol. 95, pp. 51-67, 2016.
[CrossRef] [Web of Science Times Cited 279] [SCOPUS Times Cited 420]
 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 208] [SCOPUS Times Cited 267]
 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 378] [SCOPUS Times Cited 462]
 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 1595] [SCOPUS Times Cited 2124]
 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 609] [SCOPUS Times Cited 1029]
 F. Ramezani, S. Lotfi, "Social-based algorithm", Applied Soft Computing, vol. 13, pp. 2837-2856, 2013.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 26]
 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 7]
 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 114] [SCOPUS Times Cited 188]
 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 1104] [SCOPUS Times Cited 1294]
 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 1014] [SCOPUS Times Cited 1245]
 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.
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