Cloud resource scheduling algorithm based on improved LDW particle swarm optimization algorithm

Cloud computing resource scheduling is a complex NP problem and difficult to solve. In order to shorten task completion time in cloud resource scheduling, an improved LDW-PSO (linearly decreasing weight-particle swarm optimization) algorithm is proposed. Firstly, for the fact that PSO algorithm is easy to fall into local convergence, based on the linearly decreasing weight strategy, the constant disturbance is added to increase the inertia weight, for the purpose of getting rid of local search and begining global search. Secondly, in order to avoid the situation that particles highly gather around the optimal particles, resulting in being similar and damaging the diversity of particle swarm, thus, by changing inertia weight mixed with random individuals Adaptively in a certain probability, it could better maintain the diversity of population. Finally, through different simulation tests on Matlab2010a platform, proving that the improved LDW-PSO algorithm can get a more accurate solution and optimize completion time in cloud computing resource scheduling.