基于RDC-PSOA的复杂自动化控制最优任务规划方法研究

Based on RDC-PSOA Complex Automation Control Optimal Task Planning Method Research

在复杂的自动化控制模型中进行最优任务规划数据推荐时,容易出现只将任务分配给最先执行的处理器的情况,导致个别处理器上任务分配过多,造成整体时间跨度增加,提出一种基于推荐数据特点粒子群优化(recommended data characteristics-particle swarm optimization algorithm:RDC-PSOA)的复杂自动化控制最优任务规划方法,以一群随机粒子为初始解,依据复杂自动化控制模型中任务规划数据推荐问题的特点,在粒子群算法的基础上,重新塑造粒子描述形式,对粒子的位置与速度进行编码,将粒子群算法映射到离散空间,通过迭代获取全部可能的自动化控制任务规划方案,实现数据的有效推荐;仿真实验结果表明,所提方法不仅具有很强的收敛能力,而且数据推荐完成时间短,性能优越。

In complex automation control model for the optimal mission planning data recommended, prone to only the processor＇s case will he assigned to the first task execution, lead to individual processors task allocation is overmuch, cause the overall time span increases, based on particle swarm optimization algorithm is a kind of complex optimal method recommended mission planning data of automatic control, with a group of random particles as the initial solution, based on the mission planning in complex automation control model the characteristics of the data suggested problems, on the basis of the particle swarm algorithm, reshape particle description form, to encode the particle＇s position and velocity, the particle swarm algorithm is mapped to a discrete space, through iteration to get all the possible mission planning scheme of automatic control, realizes the data effectively is recommended. The simulation results show that the proposed method not only has a strong ability of convergence, and the data recommended completion time is short, superior performance.