Efficiency Improvement of the GLOBAL Optimization Method by Local Search Changes
Abigél Mester, Dániel Zombori, László Pál, Balázs Bánhelyi · Acta Polytechnica Hungarica · 2022
There are many suitable global optimization approaches to find the minimum value of an objective function.In this paper, the improvement of the GLOBAL Optimization Method is studied, which is based on stochastic clustering.Through its three main components, which are sampling, clustering, and local search the algorithm aims to find the global minimum of the objective function.Local search methods significantly influence the efficiency of the GLOBAL method.The efficiency of our proposal may be improved by dividing the system into modules and by creating new variants of both the local and line search methods.The main contribution of this work is to show the achievements of modularization and the efficiency of the new variants of both local and line search methods.