自适应概率选择模型的改进蚁群算法研究  

Research on Improved Ant Colony Algorithm for Adaptive Probability Selection Model

作　　者：郑岩 罗强[1] 王海宝[1] 王昌洪 ZHENG Yan;LUO Qiang;WANG Hai-bao;WANG Chang-hong(School of Mechanical Engineering,Chongqing Three Gorges University,Chongqing 404000 China)

机构地区：[1]重庆三峡学院机械学院,重庆404000

出　　处：《自动化技术与应用》2021年第2期6-10,共5页Techniques of Automation and Applications

基　　金：重庆市教委科技基金资助项目(编号KJ1601032);重庆三峡学院研究生创新项目(编号YJSKY1804)。

摘　　要：传统的蚁群算法在路径规划中存在收敛速度慢、易陷入局部最优解等问题。针对这些缺陷,提出一种基于自适应概率选择模型的改进蚁群算法。最后,在Matlab2016a仿真软件中构建两种地图环境,对两种算法在不同环境下的适应性和寻优能力进行仿真实验。结果表明,改进的蚁群算法的体现了更好的收敛性,在复杂环境下的最优路径和寻优时间更短。The traditional ant colony algorithm has the problems of slow convergence and easy to fall into the local optimal solution in path planning.Aiming at these defects,an improved ant colony algorithm based on adaptive probability selection model is proposed.Finally,two map environments are built in Matlab2016 a simulation software,and the simulation and experiment of the adaptability and optimization ability of the two algorithms in different environments are carried out.The results show that the improved ant colony algorithm embodies better convergence,and the optimal path and optimization time are shorter in complex environments.

关 键 词：蚁群算法 路径规划 自适应 概率选择因子 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391[自动化与计算机技术—控制科学与工程]