An Improved Gorilla Troops Algorithm for Real-World Engineering Problems

There are many meta-heuristic algorithms inspired by nature for solving optimization problems. One of these algorithms is the Gorilla Troop Optimization (GTO) algorithm, which has been recently proposed for solving continuous optimization problems and is inspired by the behavior of gorilla troops in nature. GTO includes various mechanisms to utilize the exploration and exploitation processes efficiently. Due to this feature, although the success varies according to the problem size, it is generally known that GTO is a successful algorithm. In this study, levy flight and roulette fitness distance balance (LRFDB) are added to the basic GTO to explore the search space more efficiently. With this method added to GTO, the algorithm is named LRFDB-GTO. Thus, it is aimed to strengthen the global search capability of the algorithm. The performance of LRFDBGTO was analyzed on test functions. Then it was applied to real world problems. The results of LRFDB-GTO and the original GTO are compared in tables. At the same time, the convergence curves and box plots of the algorithms are presented in figures. The experimental results show that the LRFDB-GTO algorithm performs better than the original GTO.