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.

Keywords

• Optimizasyon
• Metasezgisel algoritma
• Test fonksiyonları

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.

Keywords

• Optimization
• Metaheuristic algorithm
• Test functions

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