摘要
针对传统蚁群算法的易陷入局部最优、求解精度低的缺点,提出了一种改进的粒子群-蚁群算法进行最优路径的求解。该算法采用具有线性递减惯性权重系数的粒子群算法进行路径预规划,由此得到蚁群算法的初始信息素分布;同时,通过在蚁群算法中引入了新的启发函数、线性递减的挥发系数和按路径长度排序的信息素增量系数,使算法的收敛速度得到提高。实验结果表明,该算法在两种环境下路径长度的误差分别为0%和0.9297%,与传统算法相比,该算法具有更高的求解精度。
Aiming at the shortcomings of traditional ant colony algorithm,such as easy to fall into local optimum and low precision of solution,this paper proposes an improved particle swarm optimization and ant colony algorithm to calculate the optimal path.In this algorithm,particle swarm optimization algorithm with linear decreasing inertia weight coefficient is used for path pre-planning,so as to obtain the initial pheromone distribution of ant colony algorithm.At the same time,by introducing new heuristic function,linear decreasing volatility coefficient and pheromone increment coefficient arranged according to path length into ant colony algorithm,the convergence speed of the algorithm is improved.Experimental results show that the path length error of the algorithm in two environments is 0%and 0.9297%respectively.Compared with the traditional algorithm,this algorithm has higher accuracy.
作者
张真诚
Zhang Zhencheng(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《电子测量技术》
北大核心
2021年第8期65-69,共5页
Electronic Measurement Technology
关键词
路径规划
粒子群算法
蚁群算法
启发函数
信息素增量系数
path planning
particle swarm optimization
ant colony algorithm
heuristic function
pheromone increment coefficient
