一种改进的网络控制系统中混合量子进化算法  

Hybrid quantum evolutionary algorithm applied to improved networked control system

作　　者：屈正庚[1] 

机构地区：[1]商洛学院计算机科学系,陕西商洛726000

出　　处：《现代电子技术》2013年第8期1-3,共3页Modern Electronics Technique

基　　金：国家自然科学基金项目(61003061);商洛学院科学与技术研究(10SKY018)

摘　　要：提出了一种针对双层网络学习系统体系结构的新的混合量子克隆进化算法(HQCEA)。这种特殊的体系结构能达到更好的控制性能、更好的抗干扰能力并增强了对各种环境的适应性,优化了网络传输周期,增强了函数求解空间的多样性,并能有效地避免陷入局部最优解。因此,网络资源得到合理分配以减少延迟和丢包,改进带有通信约束的网络利用。根据结果表明:HQCEA方法克服了传统QEA方法的缺点,而且能够在更短的时间内成功处理多峰连续函数以及复杂装置。A new hybrid quantum clone evolutionary algorithm （HQCEA） is proposed in allusion to two-layer network learn- ing control system （NLCS） architecture. This special architecture can achieve better control performance and better interference rejection, and strengthen the adaptability to varying environment. The proposed scheduling algorithm HQCEA optimized the net- work transmission period, increased the diversity of the solution space of functions and avoided trapping into local peak effective- ly. As thus, network resources can be allocated reasonably to reduce delay and packet losing, improve network utilization with communication constraints. According to the simulation results, the HQCEA overcomes the shortcoming of the traditional QEA, and can deal with the continuous functions with multi-peak and complex plant successfully in a shorter time.

关 键 词：多目标优化 混合量子进化算法 网络控制系统 混合量子克隆进化算法 

分 类 号：TN911.34[电子电信—通信与信息系统]