改进量子行为粒子群算法求解武器目标分配问题

Quantum-Behaved Particle Swarm Algorithm on Weapon Target Assignment

为了提高武器目标分配(WTA)问题的求解效率和性能,提出一种求解武器目标分配问题的改进量子粒子群优化算法.首先,通过定义粒子进化速度及粒子聚集度,将惯性权重表示为粒子进化速度和粒子聚集度的函数,使惯性权重具有自适应性.其次,将慢变函数引入传统位置更新公式中,有效地克服陷入局部最优解的问题.最后,以分配各类武器迎击来袭目标的失败概率最低为目标,建立多种类型武器目标分配问题模型.仿真实验表明,提出的算法能快速给出武器目标分配问题的最好或较好分配方案;能高效地求解武器目标分配问题.

In order to improve the solving efficiency and performance of Weapon Target Assignment （WTA）, this paper puts forward a kind of improved quantum-behaved particle swarm optimization algorithm for solving WTA. First, by defining particle evolution speed and particle aggregation degree, the inertia weight is expressed as the function of particle evolution speed and particle aggregation degree, making the inertia weight have self-adaptivity. Secondly, the slowly varying function is introduced into the traditional location updating formula, effectively overcoming the problem of the partial optimization. Finally, a multiple weapons target assignment is built to meet the target of the minimum failure probability in allocating weapons and shooting all targets. Simulation results indicate that the new algorithm can get the optimal or suboptimal solution to WTA problems, that is, effectively solve WTA problems.