摘要
针对传统的蚁群优化算法存在易陷入局部最优解,搜索时间过长等问题,提出了一种改进的蚁群优化算法。通过建立栅格地图模型,采用状态转移规则结合轮盘赌的方法对下一节点进行选择,对陷入死锁的蚂蚁采用回退策略,避免陷入局部最优。同时改进信息素增强系数、改善信息素挥发因子、建立信息素因子与所需启发函数因子之间的互锁关系,缩短最短路径的长度,减少算法的迭代次数,提高算法的收敛速度。运用MATLAB进行仿真,仿真结果表明:改进后的蚁群算法明显优于传统的蚁群算法。
An improved ant colony optimization algorithm is proposed,aiming at the problems that traditional ant colony optimization algorithm is easy to fall into the local optimal solution and the search time is too long. Firstly,by establishing a grid map model,the state transition rule is combined with the method of roulette to select the next node,and the fallback strategy is adopted for the ants that are deadlocked to avoid falling into local optimum. At the same time,improve the pheromone enhancement coefficient,improve the pheromone volatilization factor,establish the interlock relationship between the pheromone factor and the required heuristic function factor,shorten the length of the shortest path,reduce the iteration number of the algorithm,and improve the convergence speed of the algorithm. Finally,using MATLAB to simulate,the simulation results show that the improved ant colony optimization algorithm is significantly better than the traditional ant colony algorithm.
作者
刘永建
曾国辉
黄勃
LIU Yongjian;ZENG Guohui;HUANG Bo(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《传感器与微系统》
CSCD
2020年第4期56-58,62,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61603242)
机械电子工程学科建设项目(2018XK-A-03)
江西省经济犯罪侦查与防控技术协同创新中心开放项目(JXJZXTCX-030)。
关键词
改进蚁群优化算法
栅格法
轮盘赌
路径规划
机器人
improved ant colony optimization algorithm
grid method
roulette
path planning
robot
