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

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

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

Onur İnan
0000-0003-4573-7025
Türkiye

Publication Date

June 3, 2023

Submission Date

December 2, 2022

Acceptance Date

March 4, 2023

Published in Issue

Year 2023 Volume: 26 Number: 2

DOI

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

IZ

https://izlik.org/JA69GH35CG

Cite

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

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