任务调度算法中新的自适应惯性权重计算方法

发布时间：2018-07-30 07:34

【摘要】：粒子群算法(particle swarm optimization,PSO)是解决云计算环境中工作流系统的任务调度优化问题的主流智能算法.然而基于传统自适应惯性权重的粒子群任务调度算法易陷入局部最优,导致调度方案的执行时间与费用较高.因此,通过改进单个粒子的成功值计算方法,提出了一种新的自适应惯性权重计算方法 NAIWPSO(new adaptive inertia weight based particle swarm optimization).该方法通过比较每个粒子的适应度与全局最优值,可以更加精确描述粒子状态,进而提高了权重的自适应性.在新惯性权重基础上,提出了一种解决云工作流系统中任务调度优化问题的改进粒子群算法.新权重可以更准确的调整粒子速度,使算法更好地平衡粒子全局与局部搜索,避免陷入局部最优,获得执行费用更优的调度方案.实验表明,与5种已有惯性权重算法比较,新算法收敛稳定、适应度最低、执行费用平均减少18%.
[Abstract]:Particle swarm optimization (particle swarm) is the main intelligent algorithm to solve the task scheduling optimization problem of workflow system in cloud computing environment. However, the particle swarm optimization (PSO) task scheduling algorithm based on traditional adaptive inertial weights is prone to fall into local optimum, which leads to higher execution time and higher cost. Therefore, by improving the method of calculating the successful value of a single particle, a new adaptive inertial weight calculation method, NAIWPSO (new adaptive inertia weight based particle swarm optimization)., is proposed. By comparing the fitness of each particle with the global optimal value, the method can more accurately describe the state of the particle and improve the self-adaptability of the weight. Based on the new inertia weight, an improved particle swarm optimization algorithm is proposed to solve the problem of task scheduling optimization in cloud workflow systems. The new weight can adjust particle velocity more accurately, so that the algorithm can balance particle global and local search better, avoid falling into local optimum, and obtain a scheduling scheme with better execution cost. The experimental results show that compared with the existing inertial weight algorithms, the new algorithm has stable convergence, the lowest fitness, and the average cost of execution is reduced by 18%.
【作者单位】：安徽大学计算机科学与技术学院;
【基金】：国家自然科学基金项目(61300169) 安徽省教育厅自然科学研究重点项目(KJ2016A024)~~
【分类号】：TP18

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