现代战术网中的业务越来越复杂,现有的网络控制方案难以有效支持。软件定义网络(Software Defined Network,SDN)架构可以提高战术网灵活性,但由于战术网脆弱的无线环境特征及SDN的集中式控制特点,该架构存在很高的单点故障风险,控制信...现代战术网中的业务越来越复杂,现有的网络控制方案难以有效支持。软件定义网络(Software Defined Network,SDN)架构可以提高战术网灵活性,但由于战术网脆弱的无线环境特征及SDN的集中式控制特点,该架构存在很高的单点故障风险,控制信道的失效会导致整个网络崩溃。针对这一问题,提出了一种基于协议无感知转发(Protocol Oblivious Forwarding,POF)的动态可伸缩控制方案。采用带内带外混合控制的方法,可以在环境高度动态的战术网中实现韧性控制。当带外控制信道失效后,网络可以基于带内控制与数据平面自定义协议相结合的方式,继续维持一定的网络功能。实验结果表明,本方案对于不稳定的战术网有较强的适应能力,在带外控制失效信道时仍能保障网络可用并使时延保持在0.1 s左右的范围。展开更多
Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable D...Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable DE(SSDE) algorithm,is proposed.Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm.Meanwhile,to enhance the convergence rate,vectors selected randomly with the optimal fitness values are introduced to guide searching direction.Benchmark problems are used to verify this algorithm.Compared with other well-known DE algorithms,experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.展开更多
基金National Natural Science Foundation of China (No. 70971020)
文摘Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable DE(SSDE) algorithm,is proposed.Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm.Meanwhile,to enhance the convergence rate,vectors selected randomly with the optimal fitness values are introduced to guide searching direction.Benchmark problems are used to verify this algorithm.Compared with other well-known DE algorithms,experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.
