The use of local information sharing on soccer game optimization

Abstract

Research that focuses on the performance improvement of metaheuristics is important to gain understanding on the characteristic of specific algorithm. Better understanding of metaheuristics method will bring benefit when it comes to implement the methods to solve real problems. This paper studies the effect of local information sharing on the performance of soccer game optimization with suitable control parameter settings. A novel method, called soccer game optimization with local information sharing (SGOLS), has been proposed in this paper. The method implements local information derived from several nearby players to conduct move forward. Therefore, the move forward will consider the position of the ball dribbler, the previous best player position as well as the position of players nearby. The proposed method is evaluated based on 20 unconstraint continuous problems, consisting of unimodal function and multimodal functions, and compared to SGO, PSO and DE. The experiment result reveals that the local information sharing could enhance intensification search in SGOLS. The proposed methods perform better in high-dimensionality problems than SGO, PSO and DE. However, the use of local information consumes more computational times than the SGO and PSO, but it is still faster than DE.