中文摘要：

针对传统任务模型包含有效信息少,任务调度算法效率低、效果差的问题,设计了新的任务模型,提出了一种改进的粒子群算法（optimized particle swarm optimization,oPSO）。新模型增加了对任务类型及任务间迁移成本、计算单元类型及其运行成本等特性的描述。通过分析任务调度问题的需求,制定了oPSO算法的编解码方案,设定了算法各个关键部分参数及计算方法,并解决了粒子群算法（PSO）在任务调度前期收敛速度过快、后期易陷入局部最优的问题。在不同任务规模下分别对遗传算法（GA）、PSO以及oPSO算法进行调度仿真对比,当IP核数目为100左右时,oPSO算法较GA算法和PSO算法运行时间至少缩短10%,系统功耗至少降低15%,实验结果表明：oPSO算法调度效果明显优于其他算法,且各节点上功耗更为均衡,适用于解决任务调度问题。

英文摘要：

A new task model is designed and an optimized particle swarm optimization（oPSO）algorithm is proposed to solve the problem that the traditional task DAG model contains less information and the existing task scheduling algorithms based on the model are of inefficiency.The new model adds the description of task type and some real inter-task relations such as transfer cost of the task,the type of processing element（PE）and its running cost.After the requirements of task scheduling and mapping are analyzed,a new scheme of coding and decoding is formulated,key parameters of the algorithm and their calculation are proposed,and the shortcomings of the particle swarm optimization algorithm such as poor local search capacity in the early period and being easily trapped into local optima in the late period of the algorithm are overcome.Simulations and comparisons with the GA and PSO algorithms under different IP scales show that when the number of IPs is about 100,the execution time and the power consumption of the oPSO algorithm reduce at least 10% and 15%,respectively,the scheduling effect of oPSO is much better than those of other algorithms,and the energy consumption on each IPs is balanced.Thus it can be concluded that the proposed algorithm is applicable for the solution of task scheduling.