一种改进的量子粒子群算法

量子粒子群算法是将量子计算与粒子群算法相结合的一种新的优化方法。首先利用相位角进行实数编码，将动态量子旋转门引入到粒子群算法中，采用自适应变异，提出了一种改进的量子粒子群算法。然后运用Pe-nalized函数和Ackley函数测试了该算法的性能。最后将该算法应用到武器目标分配模型中，获得了最优的分配方案。仿真研究表明，该算法具有收敛速度快、搜索能力强和稳定性高的特点。

Research on an Improved Quantum Particle Swarm Optimization Algorithm

Quantum particle swarm optimization algorithm is a new algorithm based on the combination of quantum algorithm and particle swarm optimization algorithm. In this paper,an improved quantum particle swarm optimization algorithm is proposed:first,using the phase angle to encode quantum chromosome;second,the dynamic quantum rotation gate is introduced to particle swarm optimization;third,the adaptive mutation is applied. Furthermore,the performance of the algorithm is tested by Penalized function and Ackley function. Finally,the weapon target assignment model is solved by the improved quantum particle swarm optimization algorithm to obtain the optimal assignment. The simulative results show that the proposed algorithm has the characteristics of rapider convergence, powerful global search capability and better stability.