Adaptive Inertia Weight Based Particle Swarm Optimization for Resource Scheduling in Medical Cloud System

Abstract

It is a hot issue to build the cloud computing based regional medical system. In a medical cloud system, the doctor often requires the medical data of patients in difierent medical institutions, and this kind of request can be done by using the FEP (Front-End Processor) in each medical institution to implement the corresponding allocated query task. For a medical institution, how to implement the allocated task e‐ciently is then a challenge as the cost and e‐ciency of each internal FEP is difierent and need to be taken into consideration. An adaptive inertia weight based Particle Swarm Optimization (PSO) is proposed in this paper to solve the aforementioned resource scheduling problem. By tuning the inertia weight of velocity updating for particle adaptively, the searching process for optimal solution is accelerated, and a reasonable resource scheduling is achieved when the cost and e‐ciency of the query task are both taken as the fltness function. The e‐ciency of query for medical data is improved, and the experiments validate the efiectiveness of the proposed algorithm.