Optimizasyonun Optimizasyonu Yaklaşımıyla Dağılım Fonksiyonu Tabanlı Kral Kelebeği Optimizasyon Algoritmasının Performansının Artırılması

Abstract

In this study, the parameters of the distribution functions were adjusted with the optimization to optimization approach to improve the performance of the distribution function-based monarch butterfly optimization algorithm (MBO). For this, the random number generation processes, which greatly affect the flow of stochastic algorithms, were examined and the effect of distribution functions on these processes was determined. Then, the importance of parameter selection in the operation of distribution functions has been determined. It has been seen that the distribution function will be more effective with appropriate parameter selections. At this point, the distribution functions that can be used in the random number generation in the main target algorithm were tried to be determined with appropriate parameters with an upper auxiliary optimization algorithm. In conclusion; with the approach of optimization to optimization, the performance of the target algorithm has been tried to be increased and concrete results are presented in comparison with the tests made on the most used benchmark functions in the literature.