时变路网条件下车辆路径问题的自适应蚁群算法

考虑实际生活中道路路况影响运输成本及油耗率与运载量相关的因素，处理跨多时段的问题，建立时变路网条件下的车辆路径问题数学模型。通过聚类算法和节约算法构造初始解，提高求解速度；自适应地改变启发式因子和期望启发式因子，提高算法全局收敛能力；结合油耗率，将油耗率转化成信息素挥发因子，自适应更新信息素，保证其收敛速度；通过3-opt策略，提高算法的局部搜索能力。基于以上方法构造自适应蚁群算法，对8个客户规模的实例进行仿真表明提出的算法在收敛速度和寻优结果两方面略优于自适应遗传算法和蚁群算法，且因为考虑了不同运载量的油耗，为准确估计运输成本提供了方法。

Adaptive ant colony optimization for vehicle routing problem in time varying networks environment

Considering road condition affect transport cost and fuel consumption rate is associated with carry load in real life, dealing with crossing multi-period problem, vehicle routing problem in time-varying networks environment mathematical model was established. At first, constructing initial solution through cluster algorithm and saving algorithm can improve search speed, changing information heuristic factor and pheromone expectation heuristic factor adaptively can improve global convergence ability. Secondly, combining the fuel consumption rate to update pheromone adaptively ensured the convergence speed. At last, 3-opt strategy was used to improve local search ability. Thus, adaptive ant colony optimization (AACO) was constructed, using this algorithm to solve VRP for 8 clients. Experiments show that AACO is better than adaptive genetic algorithm (AGA) and (ant colony optimization) ACO in convergence speed and optimal results. Thanks to consider the different carry load of fuel consumption, it provides a more accurate for estimating transportation cost.