针对传统的蚁群算法(Ant Colony Optimization,ACO)存在收敛速度慢、效率低、容易陷入局部最优值等缺点,提出了一种新的ACO变体.首先引入了一种新的具有方向信息的启发式机制,在迭代过程中添加方向指导,进一步提高了算法的收敛速度.其次,提出了一种改进的启发式函数,以增强目标的目的性并减少路径的转弯次数.然后,引入了一种改进的状态转移概率规则,提高了搜索效率并增加了种群多样性.此外,提出了一种不均匀分布初始信息素浓度的新方法,以避免盲目搜索.形成的新的ACO变体称为改进的自适应蚁群优化算法(Modified Adaptive Ant Colony Optimization,MAACO).为了验证所提出的MAAC O的有效性,基于3种不同的空间环境模式,与现有其他7种算法进行了一系列实验.在所有的仿真实验中,所提出的MAACO生成了标准偏差为零的最短路径,并且在最小收敛生成内实现了最少的转弯次数;就3个实验而言,其与最佳现有结果相比,转弯次数平均减少了两次,平均减少比例为22.2％.实验结果证明了MAAC O在减少路径长度、减少转弯次数和提高收敛速度方面的优点和其在路径规划中的实用性和高效性.
Path Planning for Mobile Robots Based on Modified Adaptive Ant Colony Optimization Algorithm
For the traditional ACO has the disadvantages of slow convergence,low efficiency and easy to fall into local optimum,a new variant of ACO is proposed.Firstly,a new heuristic mechanism with directional information is introduced to add directional guidance in the iterative process,which further improves the convergence speed of the algorithm.Second,an improved heuristic function is proposed to enhance the purpose of the objective and reduce the number of turns in the path.Then,an improved state transfer probability rule is introduced to improve the search efficiency and increase the population diversity.In addition,a new method of unevenly distributing the initial pheromone concentration is proposed to avoid blind search.The new ACO variant is called the modified adaptive ant colony optimization algorithm(MAACO).To verify the effectiveness of the proposed MAACO,a series of experiments are conducted with seven other existing algorithms based on three different obstacle distribution environ-ment patterns.In all simulation experiments,the proposed MAACO generates the shortest path with zero standard deviation and achieves the minimum number of turns within the minimum convergence generation.For the three experiments,the average re-duction in the number of turns compared to the best available results is two,with a typical reduction of 22.2％.Experimental re-sults demonstrate the advantages of MAACO in reducing path length,reducing the number of turns and increasing the conver-gence speed and its usefulness and efficiency in path planning.
Ant colony algorithmHeuristic functionTransfer probabilityMobile robotPath planning
NETL
NSTL
万方数据
