混沌扰动模拟退火蚁群算法低碳物流路径优化

Research on low carbon logistics routing optimization based on chaotic-simulated annealing ant colony algorithm

低碳物流是目前物流配送领域的热点研究课题,也是群体智能优化算法的重要应用方向。针对物流配送中碳排放的度量方法,以VRP问题为基本模型,以碳排放成本为目标函数,建立了低碳物流配送路径优化模型。为了避免基本蚁群算法出现停滞及早熟现象,提出了带混沌扰动的模拟退火蚁群算法来求解低碳物流配送路径优化模型。该算法将混沌系统及模拟退火机制引入基本蚁群算法,避免了算法陷入局部最优,增强了全局搜索能力,提高了求解效率。通过实验仿真及对比分析可知,带混沌扰动的模拟退火蚁群算法的求解结果明显优于基本蚁群算法,表明了该算法的有效性和合理性。

Low carbon logistics is a hot research subjects of logistics routing problem currently and an important applicationdirection of swarm intelligence optimization algorithm. Concerning the method to measure the carbon emissions in logisticsdistribution, a low carbon logistics routing optimization model is established, which aimed at the less cost of carbonemissions and based on Vehicle Routing Problem(VRP). To avoid stagnation and premature phenomenon, the simulatedannealing ant colony algorithm with chaotic disturbance is proposed to solve the low carbon logistics routing optimizationmodel. The chaotic system and the simulated annealing method are introduced to the ant colony algorithm, which increasesthe global searching ability and improves the solving efficiency. Simulation and comparison results show that the simulatedannealing ant colony algorithm with chaotic disturbance gets a more satisfactory optimization result compared with antcolony algorithm, so this algorithm is effective and reasonable.