动态连续蚁群系统及其在天基预警中的应用

摘要

存在监控冲突的天基中段预警传感器调度优化是一个动态、高维、复杂多约束的非线性优化问题,其解空间的高维度与状态复杂性直接制约了智能优化算法的运用.本文以任务分解与任务复合优先权计算为基础,通过二级分离机制将解空间维度与状态复杂性降低至适于连续蚁群(continuous ant-colony optimization,CACO)处理的全局优化形态,构建出相应的优化子路径集.在此基础上,针对监控冲突导致的状态变化特性,从局部搜索递进与募集的角度提出适于传感器调度优化的MG-DCACO(double direction continuous ant-colony optimization based mass recruitment and group recruitment)算法,成功将智能优化算法应用于基于低轨星座的天基中段预警中.最后对算法的收敛性进行论证,并通过与已有规则调度算法的对比得出MG-DCACO算法可获得优于规则调度算法的全局最优解.%The scheduling method of sensors on space-based warning in middle age is a dynamic, multi-dimensional, complex-constraints nonlinear optimization problem.Considering the monitoring conflict, it is nearly impossible to use intelligent optimization algorithms in this problem.On the basis of task decomposition and task multiplex priority, by means of second-stage separating, this paper reduces the multi-dimensional and complexconstraints to a suitable area.Then, through the angles of monitoring conflict, area searching and collecting, the author puts forward a MG-DCACO ( double direction continuous ant-colony optimization based mass recruitment and group recruitment) algorithm which can be used in sensors scheduling.At last, it is proved that, the MG-DCACO is convergence and outperforming the other algorithms of sensors scheduling.