基于改进粒子群算法的车辆路径问题研究

针对标准粒子群算法在解决多维复杂优化问题中存在的“早熟”现象，以及算法后期出现的搜索精度下降、收敛速度降低等不足，对算法做出改进：引入微生物行为机制中的趋化、繁殖、迁移算子。最后，通过实例验证对比，表明改进粒子群算法在搜索效率和解的质量方面均优于遗传算法和基本粒子群算法。

Research on Vehicle Routing Problem Based on Improved Particle Swarm Optimization Algorithm

For the “premature” phenomenon of PSO in solving multidimensional complex optimization problems ,and those problems that appeared in the late of algorithm ,such as the reducing in the search accuracy and the insufficiency in the convergence rate and so on , the algorithm made improvements by introducing the chemotaxis ,breeding ,migration operator of microbial behavioral mechanisms .At last , verified by comparing instances , the improved particle swarm algorithm search efficiency in terms of quality of reconciliation are better than GA and PSO .