IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

Year 2023, Volume: 11 Issue: 2, 557 - 570, 01.06.2023

Onur İnan , Mustafa Serter Uzer

https://doi.org/10.36306/konjes.1209089

Abstract

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.

Keywords

Grey Wolf Optimization, Alpha Wolf, Whale Optimization Algorithm, Benchmark Test Functions

GRİ KURT OPTİMİZASYONUNDA KURT LİDERİNİN GELİŞTİRİLMESİ

Abstract

Optimizasyon algoritmalarının geliştirilmesi, çeşitli alanlarda performansı, geliri ve verimliliği artırma, maliyeti düşürme gibi avantajları olduğu için birçok analistin ilgisini çekmektedir. Meta-sezgisel yöntemler arasında yer alan sürü tabanlı optimizasyon algoritmaları genel olarak başarılı oldukları için daha çok tercih edilmektedir. Bu çalışmada Gri Kurt Optimizasyonunda (GWO) kurt lider sınıfı olarak da adlandırılan alfa kurt sınıfı, Balina Optimizasyon Algoritması (WOA) ile iyileştirilmiştir. Bu geliştirilmiş yönteme ILGWO adı verilir. ILGWO'yu değerlendirmek için tek modlu, çok modlu ve sabit boyutlu çok modlu kıyaslama fonksiyonlarından oluşan 23 kıyaslama testi fonksiyonu kullanılmıştır. Önerilen yöntemin 30 kez çalıştırılması sonucunda ortalama uygunluk ve standart sapma değerleri elde edilmiş ve bu sonuçlar literatür ile karşılaştırılmıştır. Literatürde karşılaştırılan algoritmalara göre ILGWO algoritması tek modlu kıyaslama fonksiyonları için 7 fonksiyondan 6'sında, çok modlu kıyaslama fonksiyonları için 6 fonksiyondan 3'ünde ve sabit boyutlu çok modlu kıyaslama fonksiyonları için 10 fonksiyondan 8'inde en uygun sonucu elde etmiştir. Dolayısıyla önerilen algoritma genellikle literatür sonuçlarından daha iyidir. Önerilen ILGWO'nun umut verici olduğu ve çeşitli uygulamalarda uygulanabileceği görülmektedir.

Keywords

Gri Kurt Optimizasyonu, Alfa Kurt, Balina Optimizasyon Algoritması, Benchmark Test Fonksiyonları, Grey Wolf Optimization, Alpha Wolf, Whale Optimization Algorithm, Benchmark Test Functions

References

• [1] S. Mirjalili, and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51-67, May, 2016.
• [2] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, Mar, 2014.
• [3] D. Karaboga, and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, no. 1, pp. 687-697, Jan, 2008.
• [4] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. L. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems-the International Journal of Escience, vol. 97, pp. 849-872, Aug, 2019.
• [5] G. G. Wang, and L. H. Guo, “A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization,” Journal of Applied Mathematics, 2013.
• [6] J. Kennedy, and R. Eberhart, "Particle swarm optimization." pp. 1942-1948.
• [7] G. Y. Zhu, and W. B. Zhang, “Optimal foraging algorithm for global optimization,” Applied Soft Computing, vol. 51, pp. 294-313, Feb, 2017.
• [8] S. Arora, and P. Anand, “Binary butterfly optimization approaches for feature selection,” Expert Systems with Applications, vol. 116, pp. 147-160, Feb, 2019.
• [9] S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper Optimisation Algorithm: Theory and application,” Advances in Engineering Software, vol. 105, pp. 30-47, 2017/03/01/, 2017.
• [10] J. Ababneh, “A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem,” Mathematical Problems in Engineering, vol. 2021, Sep 13, 2021.
• [11] T. Mahalingam, and M. Subramoniam, “A hybrid gray wolf and genetic whale optimization algorithm for efficient moving object analysis,” Multimedia Tools and Applications, vol. 78, no. 18, pp. 26633-26659, Sep, 2019.
• [12] A. Korashy, S. Kamel, F. Jurado, and A. R. Youssef, “Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm for Optimal Coordination of Direction Overcurrent Relays,” Electric Power Components and Systems, vol. 47, no. 6-7, pp. 644-658, Apr 21, 2019.
• [13] W. Chen, H. Hong, M. Panahi, H. Shahabi, Y. Wang, A. Shirzadi, S. Pirasteh, A. A. Alesheikh, K. Khosravi, S. Panahi, F. Rezaie, S. Li, A. Jaafari, D. T. Bui, and B. Bin Ahmad, “Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO),” Applied Sciences, vol. 9, no. 18, pp. 3755, 2019.
• [14] M. Ghasemi, K. Bagherifard, H. Parvin, S. Nejatian, and K. H. Pho, “Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators,” Applied Intelligence, vol. 51, no. 8, pp. 5358-5387, Aug, 2021.
• [15] M. Toz, “An improved form of the ant lion optimization algorithm for image clustering problems,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 27, no. 2, pp. 1445- 1460, 2019.
• [16] M. Khishe, and M. R. Mosavi, “Chimp optimization algorithm,” Expert Systems with Applications, vol. 149, pp. 113338, 2020/07/01/, 2020.
• [17] W. Liu, Z. Guo, F. Jiang, G. Liu, D. Wang, and Z. Ni, “Improved WOA and its application in feature selection,” PLoS One, vol. 17, no. 5, pp. e0267041, 2022.
• [18] C. Dhakhinamoorthy, S. K. Mani, S. K. Mathivanan, S. Mohan, P. Jayagopal, S. Mallik, and H. Qin, “Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease,” Mathematics, vol. 11, no. 5, pp. 1136, 2023.
• [19] X. Zhang, and S. Wen, “Hybrid whale optimization algorithm with gathering strategies for high- dimensional problems,” Expert Systems with Applications, vol. 179, pp. 115032, 2021/10/01/, 2021.
• [20] M. Mafarja, and S. Mirjalili, “Whale optimization approaches for wrapper feature selection,” Applied Soft Computing, vol. 62, pp. 441-453, Jan, 2018.
• [21] K. Price, N. Awad, M. Ali, and P. Suganthan, “The 100-digit challenge: problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization,” Nanyang Technological University, 2018.
• [22] J. M. Abdullah, and T. Ahmed, “Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process,” IEEE Access, vol. 7, pp. 43473-43486, 2019.
• [23] H. Mohammed, and T. Rashid, “FOX: a FOX-inspired optimization algorithm,” Applied Intelligence, vol. 53, no. 1, pp. 1030-1050, 2023/01/01, 2023.