摘要
信息物理系统(CPS)已经成为近年来计算机、传感器等科学研究的热点。由于任务分配的优化程度直接影响到整个系统的性能,该研究已成为CPS系统研究过程中的关键问题。针对这一问题并考虑到传统方法在任务优化性能及效率上的不足,引入并改进遗传算法,提出了一种基于改进遗传算法的CPS任务分配方法。通过动态的变异算子及变异操作保持群体的进化特性,克服了标准遗传算法(SGA)交叉操作中较大的盲目性与随机性。实验结果表明,改进的遗传算法在任务分配的收敛速度和效率上都比标准遗传算法有较大程度的提高,平均收敛速率提高了20%。
The cyber-physical system（CPS）system in recent years has become the interest point in the field of the computer and sensor science.Since the optimization of the task allocation directly determinate the overall performance of the CPS,the key issue for the CPS research is the task allocation.Aiming to solve the problems existing in the traditional methods,we introduce and update the classical genetic algorithm proposing an improved genetic algorithm based task allocating method.By using the dynamic mutation operators and related performances the evolutionary mutation population characteristics are maintained.This factor can solve the problem caused by the blindness and randomness of the standard genetic algorithm（SGA）operation.Experimental results show that the improved genetic algorithm outperforms the standard genetic algorithm for the task allocation in convergence speed and efficiency.The average convergence rate raised 20%.
出处
《电子测量技术》
2017年第4期64-67,共4页
Electronic Measurement Technology
关键词
CPS系统
任务分配
遗传算法
变异算子
CPS system
task allocation
genetic algorithms
mutation operator
