多个多弹头在轨武器平台的目标分配优化

为了解决多个多弹头在轨武器平台目标分配优化计算量较大的问题,提出了一种离散粒子群算法与禁忌搜索相结合的目标分配(discrete particle swarm optimization-taboo search, DPSO-TS)算法进行局部操作。首先建立了基于遗传算法的单个多弹头在轨武器平台拦截轨道优化模型,确定了拦截所需的速度增量和消耗燃料的质量;其次提出了以打击目标数目和单个多弹头在轨武器平台剩余燃料的最小值作为优化指标,建立了基于DPSO TS算法的目标分配优化模型;最后仿真结果表明DPSO-TS算法在保持DPSO算法收敛精度的前提下,收敛速度更快,该方法能够快速有效地解决多个多弹头在轨武器平台的目标分配优化问题。

Target assignment optimization of multiple on-orbit weaponplatforms with multiple warheads

A discrete particle swarm optimization-taboo search (DPSO-TS) algorithm local operation is proposed to reduce calculation amount of target assignment optimization of multiple on-orbit weapon platforms with multiple warheads. Firstly, the interception trajectory optimization model of single on-orbit weapon platform with multiple worheads is established by the genetic algorithm, which is used to determine the velocity increment and mass of consumption fuel. Secondly, the number of targets which can be attacked and the remaining fuel of a single weapon platform with multiple warheads are taken as indexes, and a target assignment optimization model is established by the DPSO-TS algorithm. Finally, the results of simulation show that in the premise that the DPSO-TS algorithm and the DPSO algorithm have the same accuracy, the DPSO-TS algorithm has faster convergence speed and can fast and effectively solve the targets assignment optimization problem of multiple on-orbit weapon platforms with multiple warheads.