Performance Comparison of Biology based Metaheuristics Optimization Algorithms using Unimodal and Multimodal Benchmark Functions

Authors : Fatma BELLİ, Harun BİNGÖL

Pages : 157-167

Doi:10.55525/tjst.1214897

View : 17 | Download : 13

Publication Date : 2023-03-29

Article Type : Research Paper

Abstract :Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm insert ignore into journalissuearticles values(AHA); gives better results than other metaheuristic algorithms.
Keywords : AHA, optimization, artificial intelligence, metaheuristic optimization