基于改进蚁群算法的车辆路径仿真研究

针对基本蚁群算法收敛速度慢、易陷于局部最优等缺陷,提出了一种改进蚁群算法.通过车辆的满载率调整搜索路径上的启发信息强度变化,对有效路径采取信息素的局部更新和全局更新策略,并对子可行解进行3-opt优化,在实现局部最优的基础上保证可行解的全局最优.通过对22城市车辆路径实例的仿真,仿真结果表明,改进型算法性能更优,同基本蚁群相比该算法的收敛速度提高近50%,效果显著,该算法能在更短时间内求得大规模车辆路径问题满意最优解,说明其具有较好的收敛速度和稳定性.

Vehicle Routing Simulation Based on an Improved Ant Colony Algorithm

An improved ant colony algorithm is proposed to overcome the shortcomings of the basic ant colony algorithms such as slow convergence and be prone to plunge into partial optimum. The inspired route information strength changes according to the search vehicles loaded rate. Both local information and global information are updated on the effective route. Achieving optimal local basis ensures the best possible solution by using 3-opt optimized algorithm. The examples of 22 city vehicle routing are simulated by this algorithm, and the results show that the speed of convergence nearly 50% increased compared with the basic ant algorithm. The algorithm has achieved significant results, and it requires less time to solve large-scale vehicle routing problems. It shows that this algorithm has better stability and higher convergence speed.