杜力, 徐光辉, 汪繁荣.自适应萤火虫算法改进蚁群算法的移动机器人路径规划[J].河南理工大学学报（自然科学版）,2022,41(2):124-130.

DU L, XU G H, WANG F R. An adaptive firefly algorithm improved ant colony algorithm for path planning of mobile robot[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(2):124-130.

自适应萤火虫算法改进蚁群算法的移动机器人路径规划

杜力1,2, 徐光辉1,2, 汪繁荣1,2

1.湖北工业大学 电气与电子工程学院，湖北 武汉 430068;2.湖北工业大学 太阳能高效利用与储能系统运行控制湖北省重点实验室,湖北 武汉 430068

摘要:针对传统的蚁群算法在解决移动机器人路径规划问题时存在收敛性差、搜索速度慢、过于依赖参数选择等问题，提出一种自适应萤火虫算法改进蚁群算法的混合算法。首先，在蚁群算法基础上引入萤火虫算法，对蚁群算法的核心参数进行优化；其次，针对两种算法混合后时间开销大的问题，引入精英策略和承接式相结合的信息素更新方式，并对萤火虫算法的步长因子进行自适应设计，以提高整个混合算法的求解效率和求解精度；最后，在不同的栅格环境下进行路径规划仿真实验。结果表明，混合智能算法较传统蚁群算法综合效果有明显提升。

关键词:路径规划;萤火虫算法;蚁群算法;参数优化;自适应算法

doi:10.16186/j.cnki.1673-9787.2021010073

基金项目:国家自然科学基金资助项目（61603127）

收稿日期:2021/01/17

修回日期:2021/03/05

出版日期:2022/03/15

Anadaptivefireflyalgorithm improvedantcolonyalgorithm forpathplanningofmobilerobot

DULi 1,2, XUGuanghui 1,2, WANGFanrong 1,2

1.School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan  430068 ,Hubei,China;2.Hubei Key Laboratoryof Solar Energy Efficient Utilization and Operation Control of Energy Storage System ,Hubei University of Technology,Wuhan  430068 ,Hubei,China

Abstract: Aiming at the problems of poor convergence，slow search speed，and excessive dependence on parameter selection in the traditional ant colony optimization to solve the path planning problem of mobile robot,a hybrid ant colony algorithm improved by adaptive firefly algorithm was proposed.Firstly，the firefly algorithm was introduced on the basis of the ant colony algorithm to optimize its core parameters.Secondly，to solve the problem of high time cost after the two algorithms were mixed，the pheromone update method combining the elite strategy and the continuation method was introduced，and the step factor of the firefly algorithm was adaptively designed to improve the efficiency and solving accuracy of the entire hybrid algorithm.Finally，path planning simulation experiments were carried out in different grid environments.The simulation results proved that compared with the traditional ant colony algorithm，the proposed hybrid intelligent algorithm significantly improved the overall effect.

Key words:path planning;firefly algorithm;ant colony algorithm;parameter optimization;adaptive algorithm

 自适应萤火虫算法改进蚁群算法的移动机器人路径规划_杜力.pdf