Performance Analysis of Four Metaheuristic Algorithms on Benchmark Functions

Abstract

Various metaheuristic algorithms inspired by nature are used to solve optimization problems. With the increasing number of metaheuristics, their performance on problems is gradually improving. In this paper, the performance analysis of the newly proposed metaheuristics Artificial Rabbit Optimization Algorithm (ARO), African Vulture Optimization Algorithm (AVOA), Prairie Dog Optimization Algorithm (PDO) and the well-known Genetic Algorithm (GA) were performed for the first time. ARO is modeled after rabbits’ behavioral patterns, such as detour foraging and random hiding. AVOA is developed based on the navigation and competitive behaviors of African vultures. The newly proposed final metaheuristic PDO is inspired by the survival struggle of prairie dogs. As for the popular GA, it is based on survival of the fittest. Unimodal and multimodal test functions were used during the analysis. According to the simulation results, AVOA performed better and generated more successful results compared to the others 22 times in the mean and best values. AVOA was followed by PDO and ARO, proving that the newly proposed metaheuristics will be successful on different problems.

Keywords

• Metaheuristics
• Artificial Rabbit Optimization Algorithm
• African Vulture Optimization Algorithm
• Prairie Dog Optimization Algorithm
• Genetic Algorithm

Dört Metasezgisel Algoritmanın Kıyaslama Fonksiyonları Üzerindeki Performans Analizi

Abstract

Doğadan ilham alan çeşitli metasezgisel algoritmalar, optimizasyon problemlerini çözmek için kullanılmaktadır. Metasezgisel algoritmaların sayısındaki artışla birlikte, bu algoritmaların problemlerdeki performansları da giderek iyileşmektedir. Bu makalede, yeni önerilen metasezgisel algoritmalar olan Yapay Tavşan Optimizasyon Algoritması (ARO), Afrika Akbaba Optimizasyon Algoritması (AVOA), Çayır Köpeği Optimizasyon Algoritması (PDO) ve iyi bilinen Genetik Algoritma'nın (GA) performans analizleri ilk kez gerçekleştirilmiştir. ARO, tavşanların dolambaçlı beslenme ve rastgele saklanma gibi davranış kalıplarını model alarak geliştirilmiştir. AVOA, Afrika akbabalarının navigasyon ve rekabetçi davranışlarına dayanmaktadır. Yeni önerilen son metasezgisel algoritma PDO ise çayır köpeklerinin hayatta kalma mücadelesinden esinlenilerek geliştirilmiştir. Popüler GA ise en uygun olanın hayatta kalması prensibine dayanır. Analiz sırasında tek modlu (unimodal) ve çok modlu (multimodal) test fonksiyonları kullanılmıştır. Simülasyon sonuçlarına göre, AVOA diğerlerine kıyasla 22 kez ortalama ve en iyi değerlerde daha iyi performans göstermiş ve daha başarılı sonuçlar üretmiştir. AVOA'yı PDO ve ARO takip ederek, yeni önerilen metasezgisel algoritmaların farklı problemlerde başarılı olacağını kanıtlamıştır.

Keywords

• Metasezgiseller
• Yapay Tavşan Optimizasyon Algoritması
• Afrika Akbabası Optimizasyon Algoritması
• Çayır Köpeği Optimizasyon Algoritması
• Genetik Algoritma

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