Fox Optimization Algorithm for Cec-2017 Benchmarks Problems

Abstract

In this study, the newly proposed Fox optimization algorithm (FOX) has been studied. The FOX algorithm is an algorithm that imitates the hunting movement of red foxes living in nature in snowy environments. The FOX algorithm was first proposed by Mohammed and Rashid in 2023. They demonstrated the success of FOX in CEC-2019 and engineering design problems. Since FOX is new, its success in different test functions has not been shown in the literature. In this study, the success of FOX is demonstrated in the CEC-2017 test functions, which consist of 29 test functions, which include many different problem types (unimodal, multimodal, hybrid, and composition). Three different sizes (10, 30, and 50) of FOX were run, presenting a variety of results. FOX was run independently 20 times for each CEC-2017 test function. Results are shown according to mean, standard deviation, best, worst, and time comparisons. These results can be used in FOX comparisons in different studies in the literature. The results obtained in this study constitute a source of comparison to other studies using the CEC-2017 test functions.