基于蚁群算法的电力计量资产配送多目标路径优化

Multi-Objective Path Optimization of Power Metering Assets Distribution Based on Ant Colony Algorithm

为解决传统电力企业在配电网末端配送过程中存在的问题,提出一种基于蚁群算法的多目标路径规划方法。首先,构建一个以节点数为约束条件和决策树模型的智能电表系统;其次,采用遗传算法对该智能电表进行求解,并通过仿真实验验证所提方法的有效性和可行性;最后,利用MATLAB软件编程实现该智能电表的多目标路径规划模型。结果表明:当节点数较少时,由于每个节点都有自己的最佳工作状态、最优运输成本以及最优配送时间,因此各条路线的运输成本最低且最短;随着节点数增多时,各个节点的最佳工作状态、最优运输成本与最优配送时间也随之增加,此时最优方案应选择最优路径。

In order to solve the problems existing in the end distribution process of traditional power enterprises,a multi-objective path planning method based on ant colony algorithm is proposed.Firstly,a smart meter system based on node number and decision tree model is constructed.Secondly,genetic algorithm is used to solve the smart meter,and the effectiveness and feasibility of the proposed method are verified by simulation experiments.Finally,the multiobjective path planning model of the smart meter is realized by MATLAB programming.The results show that when the number of nodes is small,because each node has its own optimal working state,optimal transportation cost and optimal distribution time,the transportation cost of each route is the lowest and shortest.As the number of nodes increases,the optimal working state,the optimal transportation cost and the optimal distribution time of each node also increase.At this time,the optimal plan should choose the optimal path.