基于子目标进化算法的要地防空武器系统优化部署  

Weapon system deployment optimization based on a sub-objective evolutionary algorithm for key-point air defense

作　　者：雷宇曜[1] 姜文志[1] 刘立佳[1] 刘涛[1] 

机构地区：[1]海军航空工程学院兵器科学与技术系,山东烟台264001

出　　处：《系统工程与电子技术》2016年第2期314-322,共9页Systems Engineering and Electronics

基　　金：军内科研项目(2014CX-C201-FW)资助课题

摘　　要：针对现代空袭、防空特点,提出要地防空远程扇形,中程、近程环形的部署方案。根据距要地不同距离范围空袭目标的特点和各型武器的性能,对部署范围进行划分。建立考虑均匀性、纵深性、靠前部署、相互掩护以及接力制导目标的高维多目标优化函数模型。从缩小搜索空间入手,在理论上证明了通过子目标函数值排序进行Pareto最优解求取的可行性,提出了子目标进化算法。同目前的高维多目标优化问题求解算法相比,显示出一定的优势。通过一个具体的部署算例,验证了所提出算法和建立的高维多目标优化模型的正确性和可行性。Based on the characteristic of modern air attack and air defense, a model that near and medium distance is ring shaped deployment and fan-shaped deployment which is far from the key-point is established. Be- cause of different distance from the key-point, there are different air attack targets. The deployment area is di- vided into three layers. Considering the fire equality, fire depth, fringe deployment, covering network, and relay guidance factors, a high-dimensional objectives function for air-defense weapon system deployment is built. By sorting the value of the sub-objective function, the searching steps are reduced. Then, a sub-objective evolution- ary algorithm （SOEA） is put forward and the theoretical feasibility is proved. The performance of the SOEA is better than other high-dimensional objectives optimization algorithms. The simulation results of a deployment example show that the SOEA and the high dimensional objectives model is correct and feasible.

关 键 词：要地防空 高维多目标优化 PARETO占优 武器系统部署 

分 类 号：E955[军事—军事工程]