Afrika Akbabaları Optimizasyon Algoritmasının Güncel Metasezgisellerle Karşılaştırmalı Analizi
Abstract
Optimizasyon problemlerinin karmaşıklığının artmasıyla birlikte yeni metasezgisel algoritmalar geliştirilmektedir. Bu algoritmalar farklı problemler üzerinde üstün performanslar sergileyerek başarılarını göstermektedir. Bu çalışmada, son zamanlarda önerilen 4 metasezgisel algoritma olan Yapay Sinekkuşu Algoritması (Artificial Hummingbird Algorithm, AHA), Afrika Akbabaları Optimizasyon Algoritması (African Vultures Optimization Algorithm, AVOA), Kerevit Optimizasyon Algoritması (Crayfish Optimization Algorithm, COA) ve Deniz Yırtıcıları Optimizasyon Algoritması’nın (Marine Predators Optimization Algorithm, MPA) 26 test fonksiyonu üzerindeki performansları karşılaştırılmıştır. Karşılaştırmalar sonucunda algoritmaların farklı fonksiyonlar üzerinde çok küçük farklarla birbirlerinden daha iyi performans gösterdiği gözlemlenmiştir. Aynı zamanda karşılaştırma sonuçları t-test istatistiksel testi ile değerlendirilmiştir. AVOA, çeşitli test fonksiyonları için çözümlerin kalitesini değerlendirmede diğer yeni metasezgisellere göre daha iyi veya karşılaştırılabilir performans göstermiştir. Gelecek araştırmalarda AVOA’nın farklı problemler üzerinde kullanılması hedeflenmektedir.
References
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- Ghaedi A., Bardsiri AK., Shahbazzadeh MJ. Cat hunting optimization algorithm: a novel optimization algorithm. Evolutionary Intelligence 2023; 16(2): 417-438.
- Hosseinzadeh M., Rahmani AM., Husari FM., Alsalami OM., Marzougui M., Nguyen GN., Lee SW. A survey of artificial hummingbird algorithm and its variants: statistical analysis, performance evaluation, and structural reviewing. Archives of Computational Methods in Engineering 2024; 1-42.
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A Comparative Analysis of African Vultures Optimization Algorithm with Current Metaheuristics
Abstract
With the increasing complexity of optimization problems, new metaheuristic algorithms are being developed. These algorithms show their success by exhibiting superior performances on different problems. In this paper, the performance of 4 recently proposed metaheuristic algorithms, namely Artificial Hummingbird Algorithm (AHA), African Vultures Optimization Algorithm (AVOA), Crayfish Optimization Algorithm (COA) and Marine Predators Optimization Algorithm (MPA) on 26 test functions are compared. As a result of the comparisons, it was observed that the algorithms outperformed each other with very small differences on different functions. At the same time, the comparison results were evaluated by t-test statistical test. AVOA has shown better or comparable performance to other recent metaheuristics in evaluating the quality of solutions for several test functions. It is aimed to use AVOA on different problems in future research.
References
- Abdollahzadeh B., Gharehchopogh FS., Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering 2021; 158, 107408.
- Arslan S. Güncel metasezgisel algoritmalarının performansları üzerine karşılaştırılmalı bir çalışma. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 2023; 11(4): 1861-1884.
- Azizi M., Talatahari S., Gandomi AH. Fire hawk optimizer: A novel metaheuristic algorithm. Artificial Intelligence Review 2023; 56(1): 287-363.
- Deng L., Liu S. Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Systems with Applications 2023; 225, 120069.
- Dorigo M., Birattari M., Stutzle T. Ant colony optimization. IEEE computational intelligence magazine, 2006; 1(4): 28-39.
- Dowsland KA., Thompson J. Simulated annealing. Handbook of Natural Computing 2012 1623-1655.
- Faramarzi A., Heidarinejad M., Mirjalili S., Gandomi AH. Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications 2020; 152, 113377.
- Ghaedi A., Bardsiri AK., Shahbazzadeh MJ. Cat hunting optimization algorithm: a novel optimization algorithm. Evolutionary Intelligence 2023; 16(2): 417-438.
- Hosseinzadeh M., Rahmani AM., Husari FM., Alsalami OM., Marzougui M., Nguyen GN., Lee SW. A survey of artificial hummingbird algorithm and its variants: statistical analysis, performance evaluation, and structural reviewing. Archives of Computational Methods in Engineering 2024; 1-42.
- Jia H., Rao H., Wen C., Mirjalili S. Crayfish optimization algorithm. Artificial Intelligence Review 2023; 56(Suppl 2): 1919-1979.
- Karaboga D. Artificial bee colony algorithm. Scholarpedia 2010; 5(3): 6915.
- Kennedy J., Eberhart R. Particle swarm optimization. IEEE In Proceedings of ICNN'95-International Conference on Neural Networks 1995; 4: 1942-1948.
- Kim TK. T test as a parametric statistic, Korean Journal of Anesthesiology 2015; 68(6): 540-546.
- Mirjalili S. Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications 2019; 43-55.
- Mishra P., Singh U., Pandey CM., Mishra P., Pandey G. Application of student’s t-test, analysis of variance, and covariance. Annals of Cardiac Anaesthesia 2019; 22(4): 407.
- Xue Y., Jia W., Zhao X., Pang W. An evolutionary computation based feature selection method for intrusion detection, Security and Communication Networks 2018.
- Yang XS. A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization. Berlin, Heidelberg: Springer Berlin Heidelberg 2010; 65-74.
- Yang XS., Deb S. Cuckoo search via Lévy flights. IEEE World congress on nature & biologically inspired computing (NaBIC) 2009; 210-214.
- Yiğit H., Ürgün S., Mirjalili S. Comparison of recent metaheuristic optimization algorithms to solve the SHE optimization problem in MLI, Neural Computing and Applications 2023; 35(10): 7369-7388.
- Zhao S., Zhang T., Ma S., Chen M. Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence 2022; 114, 105075.
- Zhao W., Wang L., Mirjalili S. Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering 2022; 388, 114194.
- Zhu D., Wang L., Zhou C., Yan S., Xue J. Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems. Expert Systems with Applications 2023; 237, 121597.
Details
| Primary Language | English |
|---|---|
| Subjects | Machine Learning (Other) |
| Journal Section | RESEARCH ARTICLES |
| Authors | |
| Early Pub Date | January 15, 2025 |
| Publication Date | January 17, 2025 |
| Submission Date | May 13, 2024 |
| Acceptance Date | September 13, 2024 |
| Published in Issue | Year 2025 Volume: 8 Issue: 1 |
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