基于云遗传算法的不确定性环境下平台资源调度适应性优化方法  

A Method for Adaptive Optimization of Platform Resource Scheduling in Uncertain Battlefield Environment Based on Cloud Genetic Algorithm

作　　者：廖梦琛 孙鹏[1] 张杰勇[1] 杜华桦[2] LIAO Mengchen SUN Peng ZHANG Jieyong BU Huahua(Information and Navigation College, Air Force Engineering University, Xi＇an 710077, China Air Force Communication Network Technology Administration Center, Beijing 100843, China)

机构地区：[1]空军工程大学信息与导航学院,西安710077 [2]空军通信网络技术管理中心,北京100843

出　　处：《空军工程大学学报（自然科学版）》2017年第4期86-92,共7页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家自然科学基金(61573017)

摘　　要：针对战场环境中不确定因素对平台资源调度方案的影响,提出了一种面向不确定事件的平台资源调度适应性优化方法。考虑平台损耗和任务处理时间2个方面的不确定性,建立以最大任务执行效率为目标函数的平台资源调度的适应性优化问题的数学模型,根据不确定事件对平台资源能力或任务属性的影响来判断是否触发调度方案的适应性优化,并基于云遗传算法设计了平台资源调度适应性优化问题数学模型的求解方法,最后通过算例进行了验证。仿真结果表明,使用云遗传算法对平台资源调度方案进行适应性优化调整,能得到更加优化的平台任务匹配关系,并从整体上缩短使命完成时间。Aimed at the problem that the uncertain factors exert an influence on platform resource scheduling scheme in the battlefield environment,aplatform resource scheduling adaptive optimization method for uncertain events is proposed.In consideration of the uncertainties of both platform loss and task processing time,a mathematical model of adaptive optimization problem of platform resource scheduling with maximum task execution efficiency as objective function is established,According to the uncertainty of the event on the platform resource capacity or task attributes to determine whether to trigger the adaptive optimization of scheduling,a method of solving the problem based on cloud genetic algorithm is designed.The simulation results show that a better relation between platform and task is obtained by using cloud genetic algorithm and the mission completion time is shorten.

关 键 词：平台资源调度 不确定性 适应性优化 云遗传算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]