摘要
针对蚁群算法加速收敛和早熟停滞现象的矛盾,提出一种基于分布均匀度的自适应蚁群算法,以求在加速收敛和防止早熟、停滞现象之间取得很好的平衡。该算法根据优化过程中解的分布均匀度,自适应地调整路径选择概率的确定策略和信息量更新策略,以数种对称和不对称TSP(traveling salesman problem)问题为例所进行的计算结果表明,该方法比一般蚁群算法具有更好的收敛速度和稳定性,更适合于求解大规模的TSP问题。
To settle the contradictory between convergence speed and precocity and stagnation in ant colony algorithm, an adaptive ant colony algorithm, which is based on the equilibrium of the ant distribution, is presented. By dynamically adjusting the influence of each ant to the trail information updating and the selected probabilities of the paths according to the equilibrium of the ant distribution, the algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. Experimental results on symmetric and asymmetric TSP show that the method presented in this paper has much higher convergence speed and stability than that of classical ant colony algorithm, and is more suitable for solving large scale TSP.
出处
《软件学报》
EI
CSCD
北大核心
2003年第8期1379-1387,共9页
Journal of Software
基金
国家自然科学基金
国家高性能计算基金
江苏省教育厅自然科学基金
南京大学计算机软件新技术国家重点实验室开放基金~~
关键词
蚁群算法
优化
旅行商问题
ant colony algorithm
optimization
traveling salesman problem
