An Improved Swarm Optimization Alogorithm

In order to overcome the basic artifical firefly algorithm(GSO) in solving problems of low precision for the multi peak function, high dimension and slow convergence. Thus, this paper comes up with a Dynamic step optimization algorithm. During the iteration, the step decrease nonlinearly. At the beginning of the optimization maintains a large step to search optimal value, enhancing global optimization ability. At the ending of it, optimization maintains a small step to improve ablity of local searching. In addition, original firely algorithm does not move anywhere, when Nit is null. this paper makes it move somewhere randomly. With it, This algorithm can avoid being mature and falling into a local value. As the same time, improved GSO can achieve a higher accuracy during Iterative process., comparing with GSO and reference literature alogorithm ASGSO. According to the simulation experiment, it shows that to some extent, the improved algorithm on covergence speend and precision are enhanced.