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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Penalized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。
Quantum particle swarm optimization is a new optimization method that combines quantum computing with particle swarm optimization. First of all, the phase angle is used to carry out real coding, the dynamic quantum revolving door is introduced into the PSO, and an improved quantum particle swarm optimization algorithm is proposed by adaptive mutation. Then use Penalized function and Ackley function to test the performance of this algorithm. Finally, the algorithm is applied to the target distribution model of weapons to obtain the optimal allocation plan. Simulation results show that this algorithm has the characteristics of fast convergence, strong searching ability and high stability.