Soccer game optimization for continuous and discrete problems

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

Soccer game optimization for continuous and discrete problem

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

Soccer game optimization for continuous and discrete problem

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

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem