Orienting mutation based fireworks algorithm

In this paper, a novel orienting mutation operator is designed to improve the performance of the fireworks algorithm, which is a recently proposed swarm intelligence algorithm for optimization. For each firework, the orienting mutation operator creates a new promising solution by adding to the firework a proper step size towards the local minimal point. By making use of the ready-made information of the optimization function, the orienting mutation operator enhances the local search ability of the algorithm. Its principles are analyzed and its effect is tested experimentally to show that it is a significant improvement.