混合改进人工鱼群算法逆向回收车辆路径问题的研究

Research on Reverse Recovery Vehicle Routing Problem Based on Hybrid Improved Artificial Fish Swarm Algorithm

以电子废弃物回收为例,考虑车辆路径问题(VRP),以运输成本、时间窗下的惩罚成本和碳排放成本最小为目标,构建多目标VRP非线性规划模型,并提出用混合改进人工鱼群算法对该模型进行求解。在确定初始人工鱼群规模前引入ε-约束法,实现人工鱼群规模的自适应生成并加快算法收敛速度。通过对人工鱼群算法视野、步长及拥挤度因子的自适应改进以改善算法易陷入局部最优解的缺陷。利用算例仿真实验验证设计的混合改进人工鱼群算法在求解精度和收敛速度上的优越性。结果表明,该算法具有较强的全局寻优能力,能有效地解决逆向回收车辆路径优化问题,可为电子废弃物逆向回收车辆路径规划提供参考建议。

Taking electronic waste recycling as an example,considering vehicle routing problem(VRP),and with transportation cost,penalty cost under time window and carbon emission cost as the minimum objectives,a multi-objective VRP nonlinear programming model is constructed,and the hybrid improved artificial fish swarm algorithm is used to solve the model.Before determining the size of the initial artificial fish swarm,ε-Constraint method is introduced to realize the adaptive generation of artificial fish swarm size and accelerate the convergence speed of the algorithm.The visual field,step size and crowding factor of artificial fish swarm algorithm are improved adaptively to remedy the defect that the algorithm is easy to fall into the local optimal solution.The simulation test verifies that the hybrid improved artificial fish swarm algorithm is superior in the solution accuracy and convergence speed.The results show that the algorithm with strong global optimization can effectively solve the problem of the path optimization of reverse recovery vehicles,and provide reference suggestions for the path planning of the reverse recycling vehicles of electronic waste.