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

Year 2023, Volume: 26 Issue: 2, 462 - 475, 03.06.2023

Mustafa Serter Uzer , Onur İnan

https://doi.org/10.17780/ksujes.1213693
Cited By: 1

Abstract

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.

Keywords

Balina optimizasyon algoritması gri kurt optimizasyonu kıyaslama test fonksiyonları

References

  • 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
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  • 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
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  • 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
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  • 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
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  • 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.

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

Year 2023, Volume: 26 Issue: 2, 462 - 475, 03.06.2023

Mustafa Serter Uzer , Onur İnan

https://doi.org/10.17780/ksujes.1213693
Cited By: 1

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

Benchmark test functions whale optimization algorithm grey wolf optimization.

References

  • 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.
There are 25 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Computer Engineering
Authors

Mustafa Serter Uzer 0000-0002-8829-5987

Onur İnan 0000-0003-4573-7025

Publication Date June 3, 2023
Submission Date December 2, 2022
Published in Issue Year 2023Volume: 26 Issue: 2

Cite

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
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https://doi.org/10.3390/axioms13010033
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