摘要： 运用罚函数法将约束优化问题转化为无约束优化问题，同时采用实数编码方案，将离散的车辆路径问题转化成准连续优化问题，在此基础上，用改进的粒子群优化算法求解最优值。改进的粒子群算法引入了杂交PSO模型和变异算子。仿真实验结果表明，该算法在保持粒子种群多样性、提高收敛速度和搜索精度、扩大搜索范围、避免过早收敛于局部极值点等方面均更有效。

关键词: 车辆路径, 粒子群优化算法, 杂交PSO模型, 变异

Abstract: This paper converts the constrained optimization problem into unconstrained optimization problem using the penalty function, transforms the discrete vehicle routing problem into quasi-continuous optimization problem using the real number coding scheme, and uses an improved Particle Swarm Optimization(PSO) algorithm to solve the pitimum on the basis of the two methods. The improved PSO algorithm introduces hybrid PSO model and the mutation operator, and simulation results show this algorithm is more effective in maintaining the diversity of the particle population, improving the convergent speed and search accuracy, expanding the search range, avoiding converging at local maximum points and so on.

Key words: vehicle routing, Particle Swarm Optimization(PSO) algorithm, hybrid PSO model, mutation

中图分类号: 

• TP311