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    网络中心化仿真任务共同体服务选择算法研究

    Community Service Selection Algorithm for Network Simulation Task

    • 摘要: 网络中心化仿真的核心问题是如何动态地把散布在网络上的各种服务进行整合,以形成新的、满足不同用户需求的仿真任务共同体.提出了一种仿真任务共同体服务选择算法(simulation task community service selection algorithm, STCSSA),其主要思想是将仿真任务共同体的构建转换成带QoS全局约束多目标优化的服务查找问题.首先介绍了仿真任务共同体服务QoS模型,并对任务共同体服务组合流程进行了评价;接着详细介绍了STCSSA运行流程,对算法的惯性权重动态变化策略进行了设计,并提出了一种可选的变异操作方法;最后将STCSSA与其他粒子群优化算法进行了对比测试,不仅从算法性能角度验证了STCSSA在提高收敛速度及避免局部最优方面具有优势,还从算法应用角度验证了STCSSA适用于大规模仿真下的网络中心化仿真任务共同体构建.

       

      Abstract: Net-centric simulation (NCS) is a new simulation mode, which provides a brand new method to support distributed simulation in WAN. To meet different users' requirements, building a new simulation task community (STC) dynamically by integrating distributed various services on network is the key problem of NCS. A simulation task community service selection algorithm (STCSSA) is proposed. It aims at the building of STC and adopts an improved particle swarm optimization (PSO) which overcomes the shortcomings of traditional PSO. The essence of the algorithm is that the problem of STC construction is transformed into a multi-objective service selection optimization with QoS constraints. Firstly, QoS model of STC service and the evaluation of STC composition process is described. Then, STCSSA is introduced in terms of the function steps, including a dynamic inertia weight strategy and an alternative method of mutation. Finally, several experiments are conducted to test STCSSA. On the one hand, STCSSA is tested with typical functions to prove the proposed algorithm can not only improve the convergence speed of service options, but also avoid the algorithm going into a local optimum. On the other hand, practical application is presented in detail. STCSSA is utilized to help a military STC search simulation services. Compared with some other PSO algorithms, STCSSA can effectively support large-scaled STC construction in NCS environment.

       

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