Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization

Yıl 2020, Cilt: 24 Sayı: 6, 1252 - 1264, 01.12.2020

Alper Köybaşı , İrfan Yazici

https://doi.org/10.16984/saufenbilder.788681

Cited By: 1

Öz

In this study, Grey Wolf Optimization (GWO), which is a new method with swarm intelligence is compared with another metaheuristic optimization method, Particle Swarm Optimization (PSO), using optimization benchmark functions. Simulation studies on test functions are presented as a table by obtaining mean, standard deviation, best and worst values. In addition, the effects of population and iteration number change on the GWO algorithm are presented in separate tables. The GWO algorithm has establish a good balance between exploration and exploitation. Simulation studies have shown that GWO has better convergence performance and optimization accuracy.

Anahtar Kelimeler

Grey Wolf Optimization, Metaheuristic Optimization, Particle Swarm Optimization

Kaynakça

• P. Erdoğmuş and E. Yalçın, “Parçacık Sürü Optimizasyonu ile Kısıtsız Optimizasyon Test Problemlerinin Çözümü,” Journal of Advanced Technology Sciences, vol. 4, no. 1, pp. 14–22, 2015.
• X.S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, pp. 65-74, 2010.
• C. Blum, “Ant colony optimization: Introduction and recent trends,” Physics of Life Reviews, vol. 2, pp. 353-373, 2005.
• S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016.
• S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014.
• N. Singh and S. B. Singh, “Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance,” Journal of Applied Mathematics, vol. 2017, pp. 1-15, 2017.
• S. Cherukuri and S. Rayapudi, “Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition,” International Journal of Renewable Energy Development, vol. 6, no. 3, pp. 203-212, 2017.
• J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of ICNN'95 -International Conference on Neural Networks, Perth, WA, Australia, vol. 4, pp. 1942-1948, 1995.
• W. Elshamy, H. M. Emara and A. Bahgat, “Clubs-based Particle Swarm Optimization,” IEEE Swarm Intelligence Symposium, Honolulu, HI, pp. 289-296, 2007.
• P. Chauhan, K. Deep, and M. Pant, “Power Mutation Embedded Modified PSO for Global Optimization Problems,” Lecture Notes in Computer Science, vol. 6466, pp. 139-146, 2010.
• M. Molga and C. Smutnicki, “Test functions for optimization needs,” Available:https://www.robertmarks.org/Classes/ENGR5358/Papers/functions.pdf.
• B. Alızada, “Sürü Tabanlı Karınca Aslanı ve Balina Optimizasyonu Algoritmalarının Fiziki Tabanlı Algoritmalarla Hibritleştirilmesi,” Erciyes Üniversitesi / Fen Bilimleri Enstitüsü, Kayseri, 2019.
• G. Demir and E. Tanyıldızı, “Optimizasyon Problemlerinin Çözümünde Sinüs Kosinüs Algoritması (SKA)’nın Kullanılması,” Fırat University Journal of Science and Engineering., vol.29, no. 1, pp. 225-236, 2017.
• M. Jamil and X.S Yang, “A Literatüre Survey of Benchmark Functions for Global Optimization Problems,” Int. Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, pp. 150-194, 2013.