基于改进粒子群优化算法求解带时间窗的车辆路径问题研究

An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Time Window

针对粒子群优化算法易出现早熟收敛、陷入局部最优的问题,提出了在粒子群搜索解的过程中监控粒子健康度的方法,对健康度低的粒子进行交叉操作。该方法既保证了健康粒子继续搜索最优解,又有效地改变了非健康粒子的状态,提高了粒子群的寻优能力以及跳出局部最优解的能力。最后通过实验数据集验证了新算法,实验结果表明与标准粒子群算法相比新算法在探索潜在最优解、保持种群多样性方面具有良好的效果。

For the premature convergence which is easily falling into local optimum on the particle swarm optimization searching process,this paper proposed a crossover operation to the particle with low health degree.This method not only effectively improved the unhealthy particles and let them jump out of local optimum,but also ensured the healthy particles to continue searching for optimal solutions.Finally,the new algorithm is verified by the Benchmark problem.The experimental results show that the new algorithm proposed is competitive to solve vehicle routing problem with time window.