SCSO: A novel sine-cosine based swarm optimization algorithm for numerical function optimization

Türker TUNCER

Abstract


Many swarm optimization algorithms have been presented in the literature and these algorithms are generally nature-inspired algorithms. In this paper a novel sine-cosine based particle swarm optimization (SCSO) is presented. In SCSO, firstly particles are generated randomly in the search space. Personal best value and velocity of the particles are calculated and by using step. Calculated velocity is used for updating particles. The proposed algorithm is basic algorithm and approximately 30 rows MATLAB codes are used to implement the proposed algorithm. This short code surprisingly has high optimization capability. In order to evaluate performance and prove success of this algorithm, 14 well known numerical functions was used and the results illustrate that the proposed algorithm is successful in numerical functions optimization.

Keywords


Sine-cosine optimization; Swarm Optimization; Swarm inspired evolutionary algorithm; Numerical Functions Optimization

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References


R. Ebenhart, Kennedy, Particle swarm optimization, in: Proceeding IEEE Inter Conference on Neural Networks, 4, Perth, Australia, Piscat-away, 1995, pp. 1942–1948.

Y.Y. Lin, J.Y. Chang, C.T. Lin, Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network, IEEE Trans. Neural Netw. Learn. Syst. 24 (2) (2013) 310–321.

C. Karakuzu, F. Karakaya, M.A. Çavusoğlu, FPGA implementation of neuro-fuzzy system with improved PSO learning, Neural Netw. 79 (2016) 128–140.

K.L. Du, M.N.S Swamy, Ant colony optimization, in: Search and Optimization by Metaheuristics, Springer International Publishing, 2016, pp. 191–199.

S. Mirjalili, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl. Based Syst. 89 (2015) 228–249.

G. Li, P. Niu, Y. Ma, et al., Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency, Knowl. Based Syst. 67 (2014) 278–289.

X.S. Yang, Multiobjective firefly algorithm for continuous optimization, Eng. Comput. 29 (2) (2013) 175–184.

S. Mirjalili, SCA: a sine cosine algorithm for solving optimization problems, Knowl. Based Syst. 96 (2016) 120–133.

T. Vidal, T.G. Crainic, M. Gendreau, et al., A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows, Comput. Oper. Res. 40 (1) (2013) 475–489.

X.S. Yang, A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization, Eng. Comput. 29 (5) (2012) 464–483.

P. Civicioglu, E. Besdok, A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms, Artif. Intell. Rev. (2013) 1–32.

S. Saremi, S. Mirjalili, A. Lewis, Biogeography-based optimization with chaos, Neural Comput. Appl. 25 (5) (2014) 1077–1097.

B. Alatas, Chaotic harmony search algorithms, Appl. Math. Comput. 216 (9) (2010) 2687–2699.

F.Y. Ju, W.C. Hong, Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting, Appl. Math. Model. 37 (23) (2013) 9643–9651.

G.G. Wang, L. Guo, A.H. Gandomi, et al., Chaotic krill herd algorithm, Inf. Sci. 274 (2014) 17–34.

K. Chen, F. Zhou, A. Liu, Chaotic dynamic weight particle swarm optimization for numerical function optimization, Knowledge-Based Systems 139 (2018) 23–40.

U. Can, B. Alatas, Performance Comparisons of Current Metaheuristic Algorithms on Unconstrained Optimization Problems, Periodicals of Engineering and Natural Scinces, Vol.5, No.3, November 2017, pp. 328-340.

S. Mirjalili, The ant lion optimizer, Advances in Engineering Software, 83, (2015), 80-98.

S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving singleobjective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), (2016), 1053-1073.

S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer. Advances in Engineering Software, 69, (2014), 46-61.

S. Mirjalili, S.M. Mirjalili, A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), (2016), 495-513.

B. Alatas, E. Akin, A.B. Ozer, Chaos Embedded Particle Swarm Optimization Algorithms, Chaos, Solitons & Fractals, vol. 40, (2009), 1715-1734.

M.C. Canoğlu, B. Kurtuluş, Determination of the Dam Axis Permeability for the Design and the Optimization of Grout Curtain: An Example from Orhanlar Dam (Kütahya-Pazarlar), Periodicals of Engineering And Natural Sciences Vol. 5 No. 1 (2017), 37-43.

N. Singh, S.B. Singh, A novel hybrid GWO-SCA approach for optimization problems, Engineering Science and Technology, an International Journal 20 (2017) 1586–1601.

X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput. 3 (2) (1999) 82–102.

B. Durakovic, Design of Experiments Application, Concepts, Examples: State of the Art, Periodical of Engineering and Natural Sciences, Vol. 5, No. 3, pp. 421-439 (2017).




DOI: http://dx.doi.org/10.21533/pen.v6i2.159

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Copyright (c) 2018 Türker TUNCER

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License