A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

Mohsin Masood, Mohamed Mostafa Fouad, Ivan Glesk

Research output: Contribution to conferenceProceeding

2 Citations (Scopus)
47 Downloads (Pure)

Abstract


Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network’s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.
Original languageEnglish
Publication statusPublished - 27 Sep 2017
Event9th Computer Science & Electronic Engineering Conference - Tony Rich Teaching Centre, University of Essex, Colchester, United Kingdom
Duration: 27 Sep 201729 Sep 2017
http://ceec.uk/

Conference

Conference9th Computer Science & Electronic Engineering Conference
Abbreviated titleCEEC'17
CountryUnited Kingdom
CityColchester
Period27/09/1729/09/17
Internet address

Keywords

  • MPLS
  • GMPLS network optimization
  • particle swarm optimization
  • heurastic algorithm
  • traffic engineering
  • weight inertia

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