摘要
针对蚁群算法收敛速度慢以及易陷入局部极值的特点,提出一种多粒度模式蚁群算法,对每个蚂蚁设置不同的窗口宽度,增加蚂蚁搜索的灵活性和多样性,给出一种寻找蚁群高阶模式的方法,提高算法进化效率。提出蚁群规模的自适应变化方法,对所提算法在不同约束条件下进行收敛性分析,给出算法收敛时间的估计方法和首达时间分布的表达形式。对提出的算法利用几个典型的TSP问题与基本蚁群算法和几种改进蚁群算法进行对比分析。将提出的多粒度模式蚁群算法应用于无人机(UAV)路径规划中。研究结果表明:所提出的算法均优于基本蚁群算法,并且大部分问题均比其他几种改进蚁群算法的优。所提出的算法提高了路径规划的效率,并可以有效地规避动态威胁。
Aiming at the problem that ant colony algorithm takes a long time to converge and is easy to converge to local optimum,the multi-granularity pattern ant colony algorithm was proposed.The ants had different window sizes in the proposed algorithm to enhance the diversity of the ants,and a new pattern was also proposed in order to enhance the efficiency.The ant number could also vary according to the evolution condition.The convergence of the proposed algorithm was also analyzed in different conditions,and the convergence time and the first hitting time were given.The proposed algorithm was used for UAV path planning problem.The results show that the proposed algorithm performs better than the basic ant colony optimization(ACO) in the TSP problem and performs better in most of TSP problem compared with the reference article.The proposed algorithm is feasible and can promote the efficiency of path planning,and can also avoid moving threat efficiently.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第9期3713-3722,共10页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(61174031)
关键词
多粒度
模式
蚁群算法
收敛性
路径规划
multi-granularity
pattern
ant colony algorithm
convergence
path planning
