Research on optimization of bat algorithm based on gauss differential mutation in the cloud computing resources

Abstract

Problem of cloud computing resource optimization has always been the research hotspot, as problems of how to satisfy execution speed, average response time and system utilization rate in resource allocation to the greatest extent. The paper first established resource scheduling model of cloud computing, and second, introduced Gaussian differential mutation into individuals of bat algorithm to narrow individual search space. Theimproved algorithm (GDMBA) has accelerated convergence rate of algorithm and lowered optimization in local. Through comparison with classical test function, it is proved that the algorithm has effectively improved performance of optimizing process. In simulation platform of Cloudsim, GDMBA algorithm has made great improvement in resource scheduling efficiency and task scheduling of cloud computing and effectively improved resource scheduling capacity of cloud computing system.