IMPROVEMENT OF BELUGA WHALE OPTIMIZATION ALGORITHM BY DISTANCE BALANCE SELECTION METHOD

Authors : Serdar PAÇACI

Pages : 125-144

Doi:10.5712/yalvac.1257808

View : 21 | Download : 14

Publication Date : 2023-03-20

Article Type : Research Paper

Abstract :In this study, an improved version of the Beluga whale optimization insert ignore into journalissuearticles values(BWO); algorithm, which is a meta-heuristic optimization algorithm recently presented in the literature, is developed to provide better solutions for the problems. The fitness-distance balance insert ignore into journalissuearticles values(FDB); selection method was applied in the search processes in the BWO algorithm, which was developed by modeling the swimming, preying and falling characteristics of beluga whales. CEC2020 benchmark functions were used to test the performance of the BWO algorithm and the algorithm named FDBBWO. The algorithms were tested on these test functions for 30, 50 and 100 dimensions. Friedman analysis was performed on the test results and the performance ranks of the algorithms were determined. In addition, Wilcoxon rank sum test was used to analyze whether there were significant differences in the results. As a result of the experimental study, it is observed that the BWO algorithm improves the early convergence problem that may arise due to the lack of diversity in the search process. In this way, the possibility of getting stuck at local optimum points is reduced. In addition, the developed algorithm is compared with 3 different algorithms that have been recently presented in the literature. According to the comparison results, FDBBWO has a superior performance compared to other meta-heuristic algorithms.
Keywords : Beyaz balina optimizasyon algoritması, uygunluk uzaklık dengesi seçimi yöntemi, meta sezgisel optimizasyon