Bat Optimization Algorithm based on Cloud model and K-means clustering

In order to improve the poor stability and easily fall into local optimal problem of the bat algorithm for optimization in function,through the use of the characteristics of randomness and stable tendency of the cloud model,an bat optimization algorithm is proposed,the population is divided into three regions based on the individual fitness and the K-means clustering algorithm for adopting different frequency generation strategy,the algorithm can not only be adaptive control the scope of the search space,and can avoid the local optimal solution.The simulation results were compared with the basic bat algorithm,which indicates that the algorithm has high precision and fast searching speed in function optimization.