黄金正弦模拟退火算法求解低碳有能力约束的车辆路径问题

针对模拟退火算法局部搜索能力不强、收敛速度慢,以及接受准则的盲目性等弊端,提出一种基于黄金正弦的模拟退火算法。首先采用黄金正弦算法的遍历特点优化模拟退火算法的初始值,然后对模拟退火算法的邻域搜索进行扩充,增加基于概率的多种算子邻域搜索和记忆装置,适当提高退火温度等措施,很大程度上提高了算法的全局优化性能。通过物流运输实例对低碳和成本节约型的多目标有能力约束的车辆路径问题(capacitated vehicle routing problem,CVRP)模型求解,以寻找环境友好型绿色路径。实验仿真表明,该混合算法具有很好的优化性能,对于求解此类车辆路径问题具有很好的优化效果。

Golden sine simulated annealing algorithm for low carbon CVRP problem

Abstract: Simulated annealing algorithm based on golden sine was proposed to solve the problems of weak local search ability, slow convergence rate and blindness of acceptance criteria.Firstly, the initial value of simulated annealing algorithm was optimized by the ergodic feature of golden sinusoidal algorithm, and then the neighborhood search of simulated annealing algorithm was extended by adding a variety of probability-based neighborhood search and memory devices, appropriately increasing the annealing temperature and other measures, which greatly improves the global optimization performance of the algorithm.The solution of the low-carbon and cost-saving multi-objective CVRP model through the logistics transportation example was used to find the environment-friendly green path. The experimental simulation shows that the hybrid algorithm has a good optimization performance and has a good optimization effect for solving this kind of vehicle path problem.