NDLI: Genetic algorithm for finding minimal cost light-forest of multicast routing on WDM networks

Content Provider	Springer Nature Link
Author	Din, Der Rong
Copyright Year	2009
Abstract	Wavelength division multiplexing (WDM) is an important technique to make use of the large amount of bandwidth in optical fibers to meet the bandwidth requirements of applications. Multicast is the transmission of information from one source to multiple destinations simultaneously. Given a multicast request in a WDM network, the goal is to find a set of light-trees, the assigned wavelengths of light-trees, and construct a light-forest. In this paper, the Minimal cost multicast routing problem (MCMRP) on WDM networks with Tap-and-continue nodes is defined and studied. A new cost model which consists of thewavelength usage and communication cost is defined. The objective is to minimize the sum of the cost of used wavelengths and the communication cost of the light-forest. Specifically, the formulation for the WDM multicast routing problem is given. Because the MCMRP is NP-hard, two genetic algorithms (GAs) are proposed to solve this problem. In the proposed GAs, a path-oriented encoding chromosome is used to represent the routing paths. These routing paths are used to construct source-based light-forests to represent a feasible solution to the multicast request. Moreover, to speed up the convergence of GAs, a Farthest-first greedy heuristic algorithm is proposed and used to generate one of the initial chromosomes. Simulation results demonstrate that the proposed GAs can run efficiently.
Starting Page	195
Ending Page	222
Page Count	28
File Format	PDF
ISSN	02692821
Journal	Artificial Intelligence Review
Volume Number	29
Issue Number	3-4
e-ISSN	15737462
Language	English
Publisher	Springer Netherlands
Publisher Date	2009-11-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Light-forest Multicast routing Wavelength division multiplexing (WDM) Genetic algorithm Path-oriented encoding Computer Science Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language

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