摘要
针对传统蚁群算法在路径规划中搜索时间长,迭代速度慢等问题,提出了一种改进的蚁群优化算法。首先建立了启发函数自适应调整机制,增强了算法在搜索过程中对终点的指向性;其次加入了局部最优方向引导机制,并提出了局部方向因素强度系数、局部方向因素衰减系数,以提高局部最优方向在算法初期的引导能力,降低局部方向在算法后期的影响,并构建了新的路径选择概率。仿真结果表明,改进的蚁群算法在二维环境的路径规划中有较高的收敛速度。
In view of the long search time and slow iteration speed of traditional ant colony algorithm in path planning,an improved ant colony optimization algorithm is proposed. First established the heuristic function of the adaptive adjustment mechanism,enhances the algorithm in the search process of end point,then joined the local optimal direction guiding mechanism,and put forward the direction of local factors of strength coefficient,local direction attenuation coefficient,in order to improve the local optimal direction early in the algorithm guiding ability,reduce the impact in the direction of local algorithm later. A new path selection probability is constructed.The simulation results show that the improved ant colony algorithm has higher convergence speed in the path planning of two-dimensional environment.
作者
王天生
张峰
WANG Tian-sheng;ZHANG Feng(SAIC GM WULING AUTOMOBILE Co., Ltd.,Liuzhou Guangxi 545000,China;School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430070,China)
出处
《装备制造技术》
2018年第3期183-186,203,共5页
Equipment Manufacturing Technology
关键词
蚁群算法
路径规划
自适应启发函数
转移概率
ant colony optimization
path planning
adaptive heuristic function
transition probability
