[1]殷 星,魏 明.基于改进蚁群算法的 PTN 网络路径优化[J].计算机技术与发展,2020,30(12):83-87.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 015]
　YIN Xing,WEI Ming.Path Optimization of PTN Network Based on Improved Ant Colony Algorithm[J].,2020,30(12):83-87.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 015]
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基于改进蚁群算法的 PTN 网络路径优化()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年12期
页码:
83-87
栏目:
智能、算法、系统工程
出版日期:
2020-12-10

文章信息/Info

Title:
Path Optimization of PTN Network Based on Improved Ant Colony Algorithm
文章编号:
1673-629X(2020)12-0083-05
作者:
殷 星1; 魏 明2
1. 武汉邮电科学研究院,湖北 武汉 430070; 2. 武汉烽火技术服务有限公司,湖北 武汉 430070
Author(s):
YIN Xing1; WEI Ming2
1. Wuhan Research Institute of Posts and Telecommunications,Wuhan 430070,China; 2. Wuhan Fiberhome Technical Services Co.,Ltd. ,Wuhan 430070,China
关键词:
PTN 网络; 改进蚁群算法; 逻辑同路由; 网络优化; 最优路径
Keywords:
PTN network; improved ant colony algorithm; logical co-routing; network optimization; optimal path
分类号:
TP311
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 12. 015
摘要:
针对分组传送网 PTN 中存在的逻辑同路由问题进行了算法研究,采用含多个约束条件的最优路径模型,求解两节点间可达的路径作为 PTN 网络路径优化的方案。 首先对该网络优化问题, 构建出多约束最优路径的数学模型;然后鉴于蚁群算法优化路径时容易陷入局部最优解并且出现“早熟停滞”现象,设计出一种改进的蚁群算法。 通过改进基本蚁群算法中的状态转移规则、启发式函数和信息素更新规则,并根据信息素增量更新的三种模型,采用局部信息素更新与全局信息素更新相结合的方式,来提高算法搜索最优解的效率和正确率。 仿真实验结果表明,与基本蚁群算法、遗传算法和 A* 算法相比较,改进后的蚁群算法具有更好的寻优能力,并且该算法在收敛速度和寻优的准确度上均有明显提升。
Abstract:
We research the algorithm of logical co-routing in packet transport network and use the optimal path model with multiple constraints to solve the reachable path between two nodes as the path optimization scheme of the PTN. First, aiming at the network optimization problem,a mathematical model with multi-constrained optimal paths is constructed. Then,since it is easy for ant colony algorithm to fall into the local optimal solution and “premature stagnation” phenomenon occurs when it optimizes the path,an improved ant colony algorithm is designed. By improving the state transition rules,heuristic functions and pheromone update rules in the basic ant colony algorithm,and according to the three models of pheromone incremental updating,local pheromone updating    and global pheromone updating are combined to improve the efficiency and accuracy of the algorithm in searching for the optimal solution.Simulation experiments show that the improved ant colony algorithm has better optimization ability compared with basic ant colony algorithm,genetic algorithm and A* algorithm,and it has significantly improved convergence speed and optimization accuracy.

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更新日期/Last Update: 2020-12-10