一种适用于多任务多资源移动边缘计算环境下的改进粒子群算力卸载算法

为了研究移动设备在多资源复杂环境下的能量消耗问题，提出一种针对移动边缘设备计算卸载的改进粒子群算法。首先基于多环境的移动设备能耗提出一种移动设备能量消耗的计算模型；其次针对计算资源分配问题设计一种可以用于衡量分配方案优劣的适应度算法；最后提出一种改进的粒子群算法，用于求解进一步降低移动边缘设备能耗分配方案的最优解。通过使用模拟仿真软件对多种卸载策略下移动设备能耗、系统响应时间等关键指标对比表明，本文算法在满足用户响应时间的前提下，在求解降低移动设备能耗调度分配方案最优解的过程中具有更优的表现。

An improved Particle Swarm Optimization ( PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment

In order to study the energy consumption of mobile devices in multi-resource complex environment, an improved particle swarm optimization (PSO) algorithm for unloading calculation of mobile edge devices is proposed. Firstly, a computing model is proposed based on the multi-environment energy consumption of mobile devices.Secondly, a fitness algorithm is designed to measure the advantages and disadvantages of resource allocation schemes for computing resource problems.Finally, an improved particle swarm optimization (PSO) algorithm for energy allocation is presented for solving the optimal solution to further reduce the energy consumption scheduling and allocation scheme of mobile devices.The comparison of energy consumption system response time and other indicators of mobile devices under various unloading strategies by simulation software shows that the proposed algorithm has a better performance in solving the optimal solution to reduce the energy consumption scheduling and allocation scheme of mobile devices on the premise of satisfying the user response time.