基于改进粒子群算法的天基预警系统资源调度方法  

Resource Scheduling for Space-based Early Warning System Based on Improved Particle Swarm Optimization

作　　者：冯明月[1] 汤绍勋[1] 何俊[1] 王香能[1] 易先清[1] 

机构地区：[1]国防科技大学信息系统与管理学院C~4ISR重点实验室,长沙410073

出　　处：《中国电子科学研究院学报》2010年第1期97-101,共5页Journal of China Academy of Electronics and Information Technology

摘　　要：天基预警系统资源调度是一项重要而棘手的问题。对预警任务特性进行了分析,在此基础上提出一种基于关键点的任务分解方法,将其转换为可求解的组合优化问题;建立了问题的约束满足模型。针对该模型规模大、变量多的特点,设计一种具有快速求解能力的改进粒子群算法进行求解,该算法采取早熟避免机制,防止粒子群算法易产生的早熟现象。实验结果表明算法能够在给定时间内求得理想的调度方案。Resource Scheduling for Space-Based Early Warning System is an important but intractable problem. Traits of Early Warning tasks are analyzed first, and then a task decomposition method is presented to convert the tasks to a solvable combination optimization problem, which is formulated by a Constraint Satisfaction Problem（CSP） model. Since the CSP model contains many variables and has a large scale, an improved Particle Swarm Optimization（PSO） algorithm was designed. The algorithm has a high capability in solution space searching, and can avoid being premature through a convergent preventing mechanism. Experimental results show that the algorithm can obtain the desired scheduling schemes in a given short period.

关 键 词：天基预警 任务分解 约束满足 粒子群 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]