Artificial Movements Inspired for Global Optimization

A new algorithm is proposed in this study for continuous problem optimization. The algorithm implements artificial movements move off and move forward, which mimics the soccer player's movement during a game. Both movements incorporate the social learning as well as cognitive learning. The performance of the proposed method was compared to those of the genetic algorithm and the particle swarm optimization. The proposed method outperforms the PSO on multi-modal functions and unimodal functions with high dimensionality. The method also shows better performance than the genetic algorithm on most of the problems used in the experiment. The experiment results reveal that the proposed method is potentially a powerful optimization technique.