Cloud Task Scheduling Algorithm Based on Squid Operator and Nonlinear Inertia Weight

In order to better meet the user's Quality of Service(QoS) requirements in cloud computing, a SNW-PSO (Squid operator and Nonlinear inertia Weight PSO) algorithm is proposed. The algorithm tasks execution time and execution cost as the target of optimization. On the one hand, the algorithm introduces nonlinear inertia weights to ensure that the algorithm jumps out of local optimum in the process of optimizing. On the other hand, the squid operator is introduced to ensure the diversity of particle swarms and make the particles converge to the global optimal position more quickly. It makes up for the shortcomings of the traditional PSO algorithm, such as easy premature convergence, easy to fall into local optimum and low search accuracy. The simulation results show that the SNW-PSO algorithm not only converges faster but also decreases task completion time and cost as compared to the standard particle swarm optimization algorithm and the particle swarm optimization algorithm with linearly decreasing of inertia weight(LDIW-PSO).