研究论文

基于演化涌现的复杂信息网络设计优化

  • 赵东杰;何宇;杨海涛;王华;李智;赵洪利
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  • 1. 装备学院重点实验室,北京 101416;2. 63628部队,北京 101601;3. 中国航天员科研训练中心,北京 100094;4. 总装备部信息化局,北京 100720

收稿日期: 2011-12-01

  修回日期: 2011-12-20

  网络出版日期: 2011-12-28

Research on the Design Optimization of Complex Information Network Based on Evolution and Emergence

  • ZHAO Dongjie;HE Yu;YANG Haitao;WANG Hua;LI Zhi;ZHAO Hongli
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  • 1. Key Laboratory, Academy of Equipment, Beijing 101416, China;2. NO. 63628 Troop of PLA, Beijing 101601, China;3. China Astronaut Training and Research Center, Beijing 100094, China;4. Information Technology Bureau, PLA General Armament Department, Beijing 100720, China

Received date: 2011-12-01

  Revised date: 2011-12-20

  Online published: 2011-12-28

摘要

针对复杂信息网络设计优化问题,分析了复杂信息网络特性,提出了基于演化涌现的复杂信息网络设计优化方法和拓扑结构演化模型。该设计优化方法以融贯论为指导,采用系统分析、建模分析和仿真分析相结合技术途径对复杂信息网络设计优化问题进行多尺度研究,树立基于网络化思维的安全观,突出网络拓扑结构设计,强化网络可控性、安全性和健壮性,体现局域结构演化形成功能整体涌现。拓扑结构演化模型综合选取网络建设成本、时延、健壮性和吞吐量等网络工程特征参数作为网络演化动力,通过调整参数权重生成优化网络。对实测ISP路由器级网络数据的仿真实验表明,方法模型具有可行性、有效性和扩展性,可加深对复杂信息网络动力学行为认识,促进信息网络科学发展。

本文引用格式

赵东杰;何宇;杨海涛;王华;李智;赵洪利 . 基于演化涌现的复杂信息网络设计优化[J]. 科技导报, 2011 , 29(36) : 23 -27 . DOI: 10.3981/j.issn.1000-7857.2011.36.003

Abstract

Aiming at the design and optimization of complex information network, the characteristics of complex information network were analyzed, the design optimization method and topology evolving model of complex information network based on evolution and emergence were introduced. Under the direction of syncretism, the method adopts a technique with a combination of system analysis, modeling analysis, and simulation analysis to study the design and optimization of complex information network from multi-scale perspective, establishes the security view based on networked thought, highlights the design of network topological structure, enhances the controllability, security, and robustness of network, and embodies the wholeness emergence of functions caused by the local structure evolution. The evolution model of topology structure selects network cost, network delay, network robustness, and network throughput as network evolution power and adjusts their weights to generate and optimize network. Experiments based on the router-level network data of internet service provider show that the method and model are feasible, effective, and scalable. The method is able to deepen our understanding of the dynamical behavior of complex information network, and promote the scientific development of information network.
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