COMPARISON OF METAHEURISTIC ALGORITHMS WITH DIFFERENT PERFORMANCE CRITERIA

Authors : Yıldız Zoralioğlu, Sibel Arslan

Pages : 266-275

Doi:10.54365/adyumbd.1344257

View : 139 | Download : 91

Publication Date : 2023-12-31

Article Type : Research Paper

Abstract :Nature-inspired metaheuristic algorithms are widely used because they achieve successful results in difficult optimization problems. Their popularity has led to the development of new metaheuristics for solving different engineering problems. New metaheuristics lead scientific research by providing faster and more efficient results. In this study, Artificial Rabbit Algorithm (ARO), Dwarf Mongoose Algorithm (DMO) and Genetic Algorithm (GA), which are recently developed metaheuristics, are compared. According to the literature review, the performances of these three algorithms are compared for the first time. Single and multi-modal standard quality test functions were used to evaluate the algorithms. The results of the algorithms were checked by t-test to see if there is a significant difference in terms of the functions used. According to the results obtained, it was observed that ARO produced more successful results than the other algorithms compared. This shows that the newly developed metaheuristics can be used in many engineering problems.
Keywords : Metasezgisel Algoritmalar, Yapay Tavşan Algoritması, Cüce Fira vun Algoritması, Genetik Algoritma, Kalite Testi Fonksiyonları

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