An adaptive ant colony algorithm-based dynamic RWA mechanism

Peng Junhua; Shen Jianhua

doi:10.13756/j.gtxyj.2014.03.011

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An adaptive ant colony algorithm-based dynamic RWA mechanism

- An adaptive ant colony algorithm-based dynamic RWA mechanism
- Study on Optical communication Issue 3, Pages: 33-34(2014)
- 作者机构：
  
  南京邮电大学通信与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2014.03.011
  CLC： TN929.1
- Published：2014，
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[1]彭军华,沈建华.一种基于自适应蚁群算法的动态RWA算法[J].光通信研究,2014(03):33-34+37.

Peng Junhua, Shen Jianhua. An adaptive ant colony algorithm-based dynamic RWA mechanism. [J]. (3):33-34(2014)
[1]彭军华,沈建华.一种基于自适应蚁群算法的动态RWA算法[J].光通信研究,2014(03):33-34+37. DOI： 10.13756/j.gtxyj.2014.03.011.

Peng Junhua, Shen Jianhua. An adaptive ant colony algorithm-based dynamic RWA mechanism. [J]. (3):33-34(2014) DOI： 10.13756/j.gtxyj.2014.03.011.

摘要

动态RWA（路由与波长分配）问题是智能光网络的核心问题

以蚁群算法为代表的启发式算法是解决此类问题的优选方案之一。文章提出一种基于ADACO（自适应蚁群优化）算法的RWA机制

针对信息素挥发系数ρ采取自适应策略

实现了对全局信息素更新态调节。理论分析和数值仿真结果表明

与Dijkstra+FF（首次命中）算法相比

改进的ADACO+FF算法可以有效地降低网络阻塞率

规则型Mesh网络和NSFNET（国家科学基金会网络）的阻塞率最高分别降低了0.3和0.2。

Abstract

Dynamic Routing and Wavelength Assignment（RWA）is a key issue to Intelligent Optical Networks（ION）

and heuristic algorithms represented by the Ant Colony Optimization（ACO）algorithm is one of the most preferred schemes for such issues.This paper proposes an Adaptive Ant Colony Optimization（ADACO）algorithm-based RWA mechanism

which realizes state adjustment for the global pheromone updating by adopting adaptive strategies.Theoretical analysis and numerical simulation results show that compared with Dijkstra+FF algorithm

the improved ADACO+FF algorithm effectively lowers the network blocking probability and the optimal blocking probability improvement in regular MESH and NSFNET topology scenario is up to 0.3and 0.2

respectively.

关键词

智能光网络动态路由波长分配启发式算法自适应蚁群优化算法自适应策略

Keywords

IONdynamic RWAheuristic algorithmADACO algorithmsadaptive strategy

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