Soccer game optimization for continuous and discrete problem

Abstract

Soccer games optimization is a new metaheuristics method that mimics the soccer player’s movement, wherein each player decides their best positions to dribble the ball towards the goal based on the ball position and other players’ position. This paper discussed the method for continuous and discrete problems based on ‘pair cooperation’ between a player and the ball position. The algorithm is implemented in eight benchmark problems consisting of continuous unconstrained problems, continuous constrained problems and discrete problem. The performance of the algorithm for the continuous unconstrained problems is compared to two meta-heuristic algorithms, the genetic algorithm and the particle swarm optimization. The continuous constrained problems and the discrete problem are compared with the result in the literature. The experimental results show that the algorithm is a potentially powerful optimization procedure that can be applied for various optimization problems.