COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Mustafa Serter Uzer; Onur İnan

doi:10.17780/ksujes.1213693

TR EN

KIYASLAMA TEST FONKSİYONLARI İÇİN GRİ KURT OPTİMİZASYONU İLE BALİNA OPTİMİZASYON ALGORİTMASININ BİRLEŞTİRİLMESİ

Öz

Bir çok optimizasyon problemi, metasezgisel yaklaşımlar kullanılarak başarıyla ele alınmıştır. Bu yaklaşımlar sıklıkla en iyi yanıtı hızlı ve etkili bir şekilde seçebilmektedir. Son zamanlarda, metasezgisel yaklaşımların bir türü olan sürü tabanlı optimizasyon algoritmalarının kullanımı daha yaygın hale gelmiştir. Bu çalışmada, Balina Optimizasyon Algoritması (WOA) ve Gri Kurt Optimizasyonu (GWO) birleştirilerek WOAGWO adı verilen hibrit sürü tabanlı bir optimizasyon yöntemi önerilmiştir. Bu yöntem, iki algoritmanın olumlu yönlerini kullanarak daha etkin bir hibrit algoritma gerçekleştirmeyi amaçlamaktadır. WOAGWO'yu değerlendirmek için 23 kıyaslama testi işlevi kullanıldı. Önerilen yaklaşım 30 kez çalıştırılarak ortalama uygunluk ve standart sapma değerleri hesaplanmıştır. Bu sonuçlar literatürdeki WOA, GWO, Karınca Aslanı Optimizasyonu algoritması (ALO), Parçacık Sürü Optimizasyonu (PSO) ve Geliştirilmiş ALO (IALO) ile karşılaştırıldı. WOAGWO algoritması, literatürdeki bu algoritmalarla karşılaştırıldığında, 7 unimodal kıyaslama fonksiyonundan 5'inde, 6 multimodal kıyaslama fonksiyonundan 4'ünde ve 10 sabit boyutlu multimodal kıyaslama fonksiyonundan 9'unda en uygun sonuçları vermiştir. Bu nedenle, önerilen yaklaşım genel olarak literatürdeki bulgulardan daha iyi performans göstermektedir. Önerilen WOAGWO ümit verici görünmektedir ve geniş bir kullanım alanına sahiptir.

Anahtar Kelimeler

COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Abstract

Many optimization problems have been successfully addressed using metaheuristic approaches. These approaches are frequently able to choose the best answer fast and effectively. Recently, the use of swarm-based optimization algorithms, a kind of metaheuristic approach, has become more common. In this study, a hybrid swarm-based optimization method called WOAGWO is proposed by combining the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). This method aims to realize a more effective hybrid algorithm by using the positive aspects of the two algorithms. 23 benchmark test functions were utilized to assess the WOAGWO. By running the proposed approach 30 times, the mean fitness and standard deviation values were computed. These results were compared to WOA, GWO, Ant Lion Optimization algorithm (ALO), Particle Swarm Optimization (PSO), and Improved ALO (IALO) in the literature. The WOAGWO algorithm, when compared to these algorithms in the literature, produced the optimal results in 5 of 7 unimodal benchmark functions, 4 of 6 multimodal benchmark functions, and 9 of 10 fixed-dimension multimodal benchmark functions. Therefore, the suggested approach generally outperforms the findings in the literature. The proposed WOAGWO seems to be promising and it has a wide range of uses.

Keywords

Kaynakça

Ababneh, J. (2021). A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem. Mathematical Problems in Engineering, 2021.
Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Conversion and Management, 179, 362-372. https://doi.org/10.1016/j.enconman.2018.10.069
Arora, S., & Anand, P. (2019). Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 116, 147-160.
Arora, S., Sharma, M., & Anand, P. (2020). A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection. Applied Artificial Intelligence, 34(4), 292-328. https://doi.org/10.1080/08839514.2020.1712788
Faris, H., Al-Zoubi, A. M., Heidari, A. A., Aljarah, I., Mafarja, M., Hassonah, M. A., & Fujita, H. (2019). An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Information Fusion, 48, 67-83. https://doi.org/10.1016/j.inffus.2018.08.002
Fausto, F., Cuevas, E., Valdivia, A., & Gonzalez, A. (2017). A global optimization algorithm inspired in the behavior of selfish herds. Biosystems, 160, 39-55. https://doi.org/10.1016/j.biosystems.2017.07.010
Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. https://doi.org/10.1016/j.matcom.2021.08.013
Hashim, F. A., & Hussien, A. G. (2022). Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Systems, 242, 108320.

Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. L. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849-872.
Hussien, A. G., & Amin, M. (2022). A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. International Journal of Machine Learning and Cybernetics, 13(2), 309-336. https://doi.org/10.1007/s13042-021-01326-4
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697.
Karakoyun, M., Gulcu, S., & Kodaz, H. (2021). D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Engineering Science and Technology-an International Journal-Jestech, 24(6), 1455-1466. https://doi.org/10.1016/j.jestch.2021.03.011
Karakoyun, M., Ozkis, A., & Kodaz, H. (2020). A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems. Applied Soft Computing, 96, Article 106560. https://doi.org/10.1016/j.asoc.2020.106560
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks,
Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441-453.
Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805-820. https://doi.org/10.1007/s10489-017-1019-8
Mostafa, R. R., El-Attar, N. E., Sabbeh, S. F., Vidyarthi, A., & Hashim, F. A. (2022). ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets. Soft Computing. https://doi.org/10.1007/s00500-022-07115-7
Oliva, D., & Elaziz, M. A. (2020). An improved brainstorm optimization using chaotic opposite-based learning with disruption operator for global optimization and feature selection. Soft Computing, 24(18), 14051-14072. https://doi.org/10.1007/s00500-020-04781-3
Toz, M. (2019). An improved form of the ant lion optimization algorithm for image clustering problems. Turkish Journal of Electrical Engineering and Computer Sciences, 27(2), 1445-1460.
Yan, S. C., Wu, L. F., Fan, J. L., Zhang, F. C., Zou, Y. F., & Wu, Y. (2021). A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China. Agricultural Water Management, 244.
Zhang, X. M., & Wen, S. C. (2021). Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems. Expert Systems with Applications, 179, Article 115032. https://doi.org/10.1016/j.eswa.2021.115032
Zhu, G. Y., & Zhang, W. B. (2017). Optimal foraging algorithm for global optimization. Applied Soft Computing, 51, 294-313.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Mustafa Serter Uzer ^*
0000-0002-8829-5987
Türkiye

Onur İnan
0000-0003-4573-7025
Türkiye

Yayımlanma Tarihi

3 Haziran 2023

Gönderilme Tarihi

2 Aralık 2022

Kabul Tarihi

4 Mart 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 2

DOI

https://doi.org/10.17780/ksujes.1213693

IZ

https://izlik.org/JA69GH35CG

Kaynak Göster

RIS / Bibtex

APA

Uzer, M. S., & İnan, O. (2023). COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 462-475. https://doi.org/10.17780/ksujes.1213693

Cited By

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

Axioms

https://doi.org/10.3390/axioms13010033

Mayfly algorithm with elementary functions and mathematical spirals for task scheduling in cloud computing system

The Journal of Supercomputing

https://doi.org/10.1007/s11227-025-07231-9