A restoration technique incorporating multi-objective goals in WDM optical networks

Sung Woo Tak, Wonjun Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Restoration techniques available in literature have not addressed their performance in terms of significant, multiple objective goals. Some of these methods have shown good performance for a single objective function. However, restoration must consider a number of objective functions. In this paper, we evaluate existing models and their performance in an attempt to verify their performance and efficacy based on literature. Our research has found not only inefficiency in some of these methods of restoration, but a general incompatibility. Consequently, this paper proposes eight objective functions that yield objective goals significant to the optimal design of a WDM (Wavelength Division Multiplexing) optical network. Each objective function model is presented and is examined by experimentation. Four proposed restoration algorithms are evaluated KSDPR (k-Shortest Disjoint Path Restoration based on multiple uphill moves and heuristic rule), DCROS (Deep Conjectural Reinforced Optimal Search), RWWA (Random Walk-based Wavelength Assignment), and PTCI (Physical Topology Connectivity Increase). Numerical results obtained by experimental evaluation of KSDPR, DCROS, RWWA, and PTCI algorithms confirm that MTWM (objective function of Minimizing Total Wavelengths with Multi-objective goals) based on the DCROS algorithm is a technique for efficient restoration in WDM optical networks.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMario M. Freire, Prosper Chemouil, Pascal Lorenz, Annie Gravey
PublisherSpringer Verlag
Pages104-114
Number of pages11
ISBN (Print)3540235515
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3262
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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