改进型免疫量子粒子群算法求解TSP问题

Improved Immune Quantum Particle Swarm Algorithm for Solving the TSP Problem

为了克服粒子群优化算法早熟收敛以及量子粒子在进化过程中缺乏很好的方向指导的问题,受生物免疫系统与量子计算思想的启发,采用了量子技术以及免疫机制,把免疫思想应用到量子粒子群算法,提出了免疫量子粒子群算法.可以指导粒子朝着更优方向进化,提高了量子粒子群的收敛速度和寻优能力,实验结果表明,仿真实验表明所提算法具有较好的性能,在求解TSP问题时收敛速度和寻优能力都取得了更好的效果.

In order to overcome premature convergence of particle swarm optimization and evolution of particles in the direction of the lack of good guidance to the problem,by the immune system and the inspired ideas of quantum computing,quantum technology and the use of immune mechanisms to the immunity applied to the quantum particle swarm algorithm,the immune quantum particle swarm optimization.Can guide the direction of particle evolution towards better,improve the convergence rate of the quantum particle swarm optimization ability and experimental results show that the simulation results show that the algorithm has better performance,solving the TSP problem in the convergence speed and searching capability have achieved better results.