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一种基于路径目的节点数的改进蚁群算法

An Improved Ant Colony Algorithm Based on Numbers of Target Nodes on Routes
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摘要 组播技术是无线Mesh网的关键技术之一,它可以大大提高信息的传播效率。介绍了信息组播的主要步骤,指出蚁群算法是构建路由组播树的高效启发式算法。对传统的蚁群算法进行定性分析,并对其进行改进,在计算路径费用以及信息素更新时考虑路径上的目的节点个数,路径上的目的节点数越多,其信息素增长速率越快。两个例子的仿真结果表明,该改进算法与传统蚁群算法相比分别使费用和代价减少了28.57%和22.36%,改善了系统性能。 Multicast technology is one of the key technologies of wireless mesh network (MSN) and it can improve the efficiency of transmitting information significantly. This paper introduces the main steps of muhicast and points out that ant colony algorithm(ANA) is an efficient heuristic algorithm for constructing a muhicast tree. The traditional ANA is analyzed qualitatively and improved by taking the target nodes on one route under consideration when calculating the route' s cost and updating the pheromone. The more target nodes on one route, the faster the increasing rate of pheromone on this route. The simulation results of two examples show that the improved algorithm can reduce the cost and price by 28.57% and 22.36% respectively compared with ANA, thus improving the performance of muhieast system.
出处 《电讯技术》 北大核心 2014年第8期1146-1151,共6页 Telecommunication Engineering
基金 国家高技术研究发展计划(863计划)项目(2012AA12A203)~~
关键词 无线MESH网 组播路由 组播树 蚁群算法 wireless mesh network (WMN) multicast routing muhicast tree ant colony algorithm
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  • 1高海昌,冯博琴,朱利b.智能优化算法求解TSP问题[J].控制与决策,2006,21(3):241-247. 被引量:121
  • 2蔡光跃,董恩清.遗传算法和蚁群算法在求解TSP问题上的对比分析[J].计算机工程与应用,2007,43(10):96-98. 被引量:29
  • 3DORIGO M,GAMBARELLA L M.Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Trans. Evolutionary Computation, 1997,1 ( 1 ):53-66.
  • 4黄翰,郝志峰,吴春国,秦勇.蚁群算法的收敛速度分析[J].计算机学报,2007,30(8):1344-1353. 被引量:72
  • 5DORIGO M,MANIEZZO V,COLORNI A.Ant system:optimization by a colony of cooperating agents[J].IEEE Trans on Systems,Man and Cybernetics,1996,26(1):29-41.
  • 6BEKTAS T.The multiple traveling salesman problem:an overview of formulations and solution procedures[J].Omega,2006,34(33):209-219.
  • 7BONABEAU E,DORIGO M,THERAULAZ G.Inspiration for optimization from social insect behaviour[J].NATURE,2000,406(6):39-42.
  • 8SHANG Yun-wei,QIU Yu-huang.A note on the extended Rosenbrock function[J].Evolutionary Computation,2006,14(1):119-126.
  • 9STUTZLE T,HOOS H.MAX-MIN ant system[J].Future Generation Computer Systems,2000,16(8):889-914.
  • 10STUTZLE T,DORIGO M.A short convergence proof for a class of ant colony optimization algorithms[J].IEEE Trans on Evolutionary Computation,2002,6(4):358-365.

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