Runge Kutta Optimization for Fixed Size Multimodal Test Functions

In this study, it is aimed to increase the success of Runge Kutta (RUN) algorithm, which used in solution many optimization problems literature, on fixed-size test functions by changing parameter values. Optimization can be defined as making a system most efficient at least possible cost under certain constraints. For process, algorithms have been designed literature and obtain best solutions for problems. The important parts solving these are modeling problem correctly, determining parameters constraints problem, finally choosing suitable meta-heuristic algorithm objective function. Not every structure. Therefore, suitability RUN will evaluated. Theoretically, Runge-Kutta methods numerical analysis an type family closed open iterative approximations ordinary differential equations. also with inspiration from methods. order evaluate performance 10 multimodal (Shekel's Foxholes, Kowalik, Six-Hump Camel-Back, Branin, Goldstein-Price, Hartman3, Hartman6, Shekel5, Shekel7, Shekel10) found before was selected. Solutions each selected obtained values algorithm. were evaluated comparing Slime Mold Algorithm (SMA) Hunger Games Search (HGS) algorithms.