摘要
针对空间数据业务的多类型、大容量等特点,基于AOS(advanced orbiting system)虚拟信道复用技术,分析了AOS虚拟信道调度的多约束问题,建立了AOS虚拟信道调度模型,提出了一种基于自适应熵估计遗传算法(AEEGA)的AOS虚拟信道调度算法.该算法能够根据种群熵和个体适应度自适应调整交叉概率与变异概率,并设计了基于各进化算子的虚拟信道调度流程.实验结果表明,该算法能保证高优先级虚拟信道较低的包剩余量、丢包率和延时,并可保持较好的公平性,比自适应遗传算法的全局搜索能力更强,比动态优先级调度算法的总体满意度更高.
For multi-type and high-capacity of space data based on AOS （advanced orbiting system） virtual channel mul- tiplexing technology, the constraints problem on AOS virtual channel scheduling are analyzed, and an AOS virtual channel scheduling model is established. Furthermore, an AOS virtual channel scheduling algorithm based on adaptive entropy esti- mating genetic algorithm （AEEGA） is proposed. The algorithm can adaptively adjust the crossover probability and mutation probability according to populations＇ entropy and individuals＇ fitness. The virtual channels＇ scheduling flow is designed based on evolution operation. Experimental results show that the algorithm can maintain high-priority services＇ excellent perfor- mances of packets residual, packets dropping rate and delay, and keep preferable fairness for all virtual channels. Meanwhile, it has stronger global searching ability than adaptive genetic algorithm and higher overall satisfaction than dynamic priority scheduling algorithm.
出处
《信息与控制》
CSCD
北大核心
2013年第3期333-340,共8页
Information and Control
基金
国家863计划资助项目(2011AAXX04)
辽宁省教育厅科研项目(L20111217)
关键词
高级在轨系统
虚拟信道调度
遗传算法
种群熵
advanced orbiting system （AOS）
virtual channel scheduling
genetic algorithm
population entropy
