摘要
基于智能仿生计算的蚁群优化算法在路径规划问题中具有较好的应用前景,通过蚁群算法优化,实现机器人路径规划和应急救援的路径规划等。传统的基于蚁群算法的路径规划在信息素转换中容易导致信息丢失,产生局部收敛,提出一种基于信息素多目标Pareto支配的蚁群优化算法实现路径规划,利用信息素多目标Pareto集合序列的均匀遍历特性和逻辑差分变尺度特征,进行变尺度搜索,根据蚁群优化算法一次次地更新搜索空间,结合负反馈机制,通过蚂蚁的信息素转化进行路径分析,采用Pareto支配集记录下最优的食物源,蚁群在寻找食物过程中,避免了局部寻优和局部收敛,仿真结果表明,采用该算法进行蚁群优化后的路径规划避障效果较好,以较快的收敛速度和较少的迭代次数找到最优路径,收敛性好。
The ant colony optimization algorithm based on the intelligent bionic computing has a good prospect in the path planning problem, and the path planning of robot path planning and emergency rescue can be realized by using the ant colony optimization algorithm. The path planning based on ant colony algorithm is easy to lead to information loss, resulting in local convergence. The algorithm uses ant colony optimization algorithm, which is based on the multi objective Pareto algorithm. By means of the ant colony optimization algorithm, which is based on the multi objective Pareto algorithm. By means of the algorithm of ant colony optimization algorithm, which is based on the algorithm of Pareto. Path, convergence is good.
出处
《科技通报》
北大核心
2016年第6期99-103,共5页
Bulletin of Science and Technology
基金
河南省自然科学基金研究项目资助项目(编号:142300410435)
