面向电动汽车路径规划的随机竞争蚁群算法  

Random Competition Based Ant Colony Optimization for Electric Vehicle Routing Problem

作　　者：曹浩[1] 杨强 CAO Hao;YANG Qiang(School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing 210000,China)

机构地区：[1]南京信息工程大学人工智能学院,江苏南京210000

出　　处：《软件导刊》2025年第3期16-22,共7页Software Guide

基　　金：国家自然科学基金项目(62006124,62272108,U20B2061)。

摘　　要：出于环保的考虑,电动汽车在当代社会越来越受到人们的喜爱,其路径规划问题也得到了广泛研究。车辆路径规划是著名的NP难问题,电车路径规划还需考虑到充电问题。为此,提出一种改进蚁群算法,设计一种基于随机竞争的蚁群信息素更新策略,在蚁群中随机挑选一些蚂蚁,将它们随机配对在一起,从每对蚂蚁中选择一个更好的更新信息素矩阵,从而使搜索多样性与收敛性之间保持良好的平衡;同时在迭代后期增加局部搜索策略来提高最优解的求解精度。实验结果表明,与传统蚁群算法相比,改进蚁群算法在解的精度和收敛速度上更有优势。Due to environmental considerations,electric vehicles are becoming increasingly popular in contemporary society,and their path planning problems have also been widely studied.Vehicle path planning is a well-known NP hard problem,and tram path planning also needs to consider the charging problem.To this end,an improved ant colony algorithm is proposed,and a random competition based ant colony pheromone update strategy is designed.Some ants are randomly selected from the ant colony,paired together,and a better updated pheromone matrix is selected from each pair of ants,thus maintaining a good balance between search diversity and convergence;At the same time,local search strategies are added in the later stages of iteration to improve the accuracy of solving the optimal solution.The experimental results show that compared with traditional ant colony algorithm,the improved ant colony algorithm has more advantages in solution accuracy and convergence speed.

关 键 词：蚁群算法 随机竞争 路径规划 组合优化 信息素更新 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]