基于粒子群优化算法的协同空战导弹目标分配  

Missile-target Optimal Assignment of Air Combat in Coordination Based on Improved Particle Swarm Optimization Algorithm

作　　者：滕鹏[1] 张君海[2] 于雷[1] 吕慧刚[1] 

机构地区：[1]空军工程大学工程学院,陕西西安710038 [2]第二炮兵工程学院,陕西西安710025

出　　处：《空军工程大学学报（自然科学版）》2008年第5期24-28,共5页Journal of Air Force Engineering University(Natural Science Edition)

摘　　要：为了取得协同空战的最佳攻击效果,在协同攻击的过程中进行导弹-目标最优分配是一种有效的解决方法。首先运用作战效能和运筹学理论建立多目标协同攻击的导弹-目标最优分配模型,其次在分析基本粒子群优化算法特点的基础之上提出了一种改进粒子群优化算法,其中的主要改进有3点:惯性权自适应调整、粒子速度与位置自动更新以及优化策略改进。然后将该改进粒子群优化算法应用于协同空战导弹-目标最优分配问题的迭代求解。仿真结果表明所采取的改进策略加快了算法的收敛速度,提高了粒子的局部求解精度与全局寻优能力,并且与基本粒子群算法、遗传算法相比较,该改进粒子群优化算法能够更加快速、有效地求出多目标协同攻击的导弹-目标分配最优解。In order to achieve an optimal attack effect during the air combat in coordination, missile - target optimal assignment is an effective method in the process of attack in coordination. On the theoretical basis of operational efficiency and operational research, a missile - target optimal assignment mathematical model for multi - target attack in coordination is established. Then, based on the analysis of the basic particle swarm optimization （PSO） algorithm, an improved particle swarm optimization （IPSO） algorithm is proposed, and there are three improvements： 1. Adaptive adjustment of inertia weight; 2. Amelioration of particle velocity and position ; 3. Betterment of optimization strategy. And this algorithm is applied in solving the missile - target optimal assignment problem of coordination air combat （MTACAC）. The simulation result indicates that those advantageous improvements can expedite the convergence speed of the PSO algorithm and improve its local and global search ability. The IPSO algorithm, compared with the basic PSO algorithm and the genetic algorithm （GA）, is better in finding the optimum assignment solution more quickly and effectively for the multi - target attack in coordination.

关 键 词：协同空战 多目标攻击 导弹-目标分配 粒子群优化 

分 类 号：V247[航空宇航科学与技术—飞行器设计]