采用自适应概率粒子群算法的反导预警资源调度方法  

Resource Scheduling Method of Missile Defense Ear1yWarning System Based on Self-Adaptive Probability Particle Swam Optimization

作　　者：任俊亮[1] 邢清华[1] 李强[2] 贾哲[3] 

机构地区：[1]空军工程大学防空反导学院,陕西西安710051 [2]空军工程大学训练部,陕西西安710051 [3]空军指挥学院,北京海淀100097

出　　处：《空军工程大学学报（自然科学版）》2014年第6期45-48,共4页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(61272011)

摘　　要：根据反导预警资源的实际特点,采用多Agent技术研究反导预警调度问题。设计资源管理Agent和预警资源Agent,给出卫星与雷达探测目标的适宜度计算方法,以方案适宜度最大化为目标生成调度方案。为提高调度方案的时效性设计一种基于自适应概率的粒子群算法,算法中粒子的每一维分量根据方案适宜度以不同概率取值,反映粒子的思考过程。实例分析表明,该算法与现有的预警资源调度算法相比,能较快地收敛到一个较优值,使调度方案满足反导预警作战实时高效的要求。Based on the practical characteristics of the resources for missile defense early warning system,the muti-agent technology is used to study the problem of how to schedule resource of missile defense ear1 y warning system.And two agents which are resource management agent and resource agent are designed.Then,a method of fit value computation of a scheme is given,and the optimization scheme has the maximum fit value.For reducing the time of creating scheme,an improved particle swarm optimization algorithm—self adaptive probability particle swarm optimization（SAPPSO）algorithm is designed,in this algorithm,every element in a particle will be changed by the probability which decided by the fit value of scheduling scheme,this course shows how the particles think.By analyzing a real case,and through a contrast with other methods,the result shows that the SAPPSO algorithm can quickly focus on a better value,which can make the scheme meet the operational requirements.

关 键 词：反导 预警 自适应 粒子群 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]