Population diversity controlled and feedback galactic swarm optimization algorithm

Optimization is the process of finding the optimal solution for a problem in a reasonable time. Populationbased optimization algorithms aim to achieve better solutions by improving more than one solution candidate to reach a solution. The balance of exploration and exploitation capabilities of the solution candidates enables the optimization method to achieve quality solutions. To improve exploration and exploitation capabilities, population diversity control is a widely used tool. Population diversity is the diversity of the values of the position, speed and objective function of the population. Low population diversity affects the exploitation ability positively while high diversity affects the exploration ability positively. The Galactic swarm optimization method is a population-based optimization system inspired by the movements of the planets and stars. The Galactic swarm optimization method is not a direct optimization algorithm, but a framework that uses the particle swarm optimization algorithm. The particle swarm optimization algorithm is a population-based optimization algorithm that is inspired by the behavior of fishes and birds. Galactic swarm optimization method consists of two stages. The aim of the first phase is to effectively scan the search space and the aim of the second stage is to improve the solutions obtained from the first stage. In this paper, with the control of population diversity at the first stage, the exploration ability was improved and the individuals obtained from the second stage were added to the first stage and the performance of the standard galactic swarm optimization method was improved. The performance of the proposed method has been tested on standard benchmark functions, which are frequently used in the literature, and the results have been compared with the recently proposed optimization algorithms. As a result of the studies, it has been observed that the proposed method improves the performance of the standard galactic swarm optimization method.