混合量子粒子群算法求解车辆路径问题

量子粒子群算法在求解车辆路径问题时一定程度上解决了基本粒子群算法收敛速度不够快的缺点，但是量子粒子群算法仍然存在容易陷入局部最优的缺点。利用混合量子粒子群算法对车辆路径问题进行求解，运用量子粒子群算法对初始粒子群的粒子进行更新，对粒子进行交叉操作，可以提高算法的全局搜索能力，进行变异操作，可以改善算法的局部搜索能力。以Matlab为工具进行仿真实验，实验结果表明改进后的算法在求解车辆路径问题时具有良好的性能，可以避免陷入局部最优，对比量子粒子群算法和遗传算法具有一定的优势。

Hybrid quantum Particle Swarm Optimization algorithm for vehicle routing problem

Quantum Particles Swarm Optimization（QPSO） algorithm partly solves the shortcoming such that Particle Swarm Optimization algorithm rate of convergence is not fast enough, while in solving the Vehicle Routing Problem （VRP）. But there is still disadvantage. QPSO falls into local optimum easily. This paper proposes a hybrid Quantum Particle Swarm Optimization algorithm to solve the vehicle routing problem. It uses the QPSO to update particles of initial particle swarm; the crossover oper ating to particles can improve the global search ability; the mutation operating to particles can improve the local search ability. Applying Matlab as tool for simulation experiment, the experimental result shows that the improved algorithm had good perfor mance to deal with VRP. It can avoid falling into local optimum, and it is better than QPSO and genetic algorithm.