A genetic algorithm for solving large scale global optimization problems

Abstract

There are many problems in the real world that can be modeled as large scale global optimization problems. Usually, large scale global optimization problems are global optimization problems where the dimensions are greater than or equal to 1000. In this research, we propose a genetic algorithm that can be used to solve large scale optimization problems with dimensions up to 100000. To measure the capabilities of the proposed genetic algorithm, we use five different test functions. Based on the results obtained, it can be inferred that the proposed genetic algorithm can find a good solution in a fairly short time.

Demanded Scale for Modern Numerical Optimisation

“…Based on the results of an investigation of genetic algorithm applied to large scale optimization problems with dimensions up to 100000 it can be inferred that the proposed genetic algorithm can find a good solution in a fairly short time. [27] This study needs further evaluation of tasks with unknown optimal solutions.…”