A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks
A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks
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DOI：
10.1016/j.physa.2009.10.005
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发表时间：
2010-02
影响因子：
3.3
通讯作者：
Feng Yu;Yanjun Li;Tie-Jun Wu
中科院分区：
文献类型：
--
作者：
Feng Yu;Yanjun Li;Tie-Jun Wu
现实世界中的大量网络都具有无标度结构，且网络参数随时间随机变化。在无标度动态随机网络中寻找最短路径不仅是估计网络的平均最短路径长度等统计特性所必需的，而且也对传统的网络最短路径概念和路径搜索策略的设计提出了挑战。在无标度动态随机网络模型的基础上，定义了最短路径的概念，提出了一种基于时间蚁群算法的网络最短路径搜索算法。通过理论分析和计算机仿真，讨论了Taco算法的收敛特性和一些重要参数的设置，验证了该算法的有效性。
A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.