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针对反导目标分配优化问题中存在的不确定性特征,引入模糊随机规划理论.首先建立了基于模糊随机规划的反战术弹道导弹(tactical ballistic missile,TBM)的目标分配优化模型.在此基础上,构建了一种针对多约束目标分配问题的粒子编码方案,并改进传统粒子群算法的位置和速度更新方式,提出了改进型离散粒子群(improve discrete particle swarm optimization,IDPSO)算法.最后,设计了模糊随机模拟技术和IDPSO算法相结合的混合智能求解算法.仿真实例表明,混合智能算法全局寻优能力强,优化效率高,满足反TBM目标分配优化对时效性的要求.
Aiming at the uncertainty characteristics of the ABM assignment optimization problem, a stochastic programming theory is introduced.Firstly, a target assignment optimization model of tactical ballistic missile (TBM) based on fuzzy stochastic programming is established. , An improved particle swarm optimization (IDPSO) algorithm is proposed based on particle swarm optimization (CPSO) for multi-constrained object allocation and to improve the position and velocity update of traditional particle swarm optimization algorithm.Finally, The hybrid intelligent algorithm based on fuzzy stochastic simulation and IDPSO is presented.The simulation results show that the hybrid intelligent algorithm has the advantages of global optimization, high optimization efficiency and meeting the requirements of anti-TBM target allocation optimization.