An Improved Fruit Fly Optimization with Changing Step

In order to overcome the basic Fruit Fly Optimization Algorithm(FOA) in solving global optimization problems, precision is not high, slow convergence speed and easily falling into the master problem, an improved Fruit Fly Optimization Algorithm with changing step is proposed. In this paper, Exponential Distribution can be used to enhance the exploration ability of the algorithm. At the same time, decreasing step size can be used to enhance the local optimization ability of the algorithm. This improvement achieves the equilibrium between global and local optimizations effectively. This algorithm is compared with other two improved FOA algorithms and the original FOA algorithm on 6-benchmark functions, the experimental results demonstrate that the new algorithm can jump out of local optimal, then the convergence rate and the precision of the Fruit Fly Algorithm can be improved significantly.