CAJ | 학술논문

针对如何提高面向服务军事信息系统中任务工作流执行的时效性和成功概率，提出了服务资源分配的并行优化方法．首先给出了服务资源分配的系统框架，在分析服务并行执行数目、任务成功率、任务完成时间及服务执行代价之间关系的基础上，建立了服务并行优化的目标规划数学模型，并提出了一种求解该模型的改进粒子群算法（DPSO）．该算法通过引入粒子细微扰动、优化粒子飞行边界及粒子优胜劣汰等扩大搜索范围，提高获得最优解的概率．实验结果表明服务分配的并行优化及其DPSO求解算法是提高任务工作流执行成功率和时效性的有效方法．
침대여하제고면향복무군사신식계통중임무공작류집행적시효성화성공개솔，제출료복무자원분배적병행우화방법．수선급출료복무자원분배적계통광가，재분석복무병행집행수목、임무성공솔、임무완성시간급복무집행대개지간관계적기출상，건립료복무병행우화적목표규화수학모형，병제출료일충구해해모형적개진입자군산법（DPSO）．해산법통과인입입자세미우동、우화입자비행변계급입자우성렬태등확대수색범위，제고획득최우해적개솔．실험결과표명복무분배적병행우화급기DPSO구해산법시제고임무공작류집행성공솔화시효성적유효방법．
Towards how to improve the efficiency and successful probability of task-workflows in service oriented military information system, a parallel optimization method of service resource allocation was proposed. Firstly, a service resource allocation framework is offered. By analyzing the relationship of service executing number, task executing time, task successful probability and service executing cost, a target programming mathematical model for parallel optimization of service resources was established. Then, an ameliorated particle swarm optimization （called DPSO） algorithm was proposed to resolve the matbematical model. By introducing random disturbance, searching boundary optimization and survival of the fittest for the particles, DPSO extended searching scope to obtain the optimal solution with a higher probability. Experimental results show that parallel optimization method of service resource allocation and the DPSO algorithm are effective methods to improve the efficiency and successful probability of task-workflows.