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 language | English |
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Publication status | Published - 27 Sep 2017 |
Event | 9th Computer Science & Electronic Engineering Conference - Tony Rich Teaching Centre, University of Essex, Colchester, United Kingdom Duration: 27 Sep 2017 → 29 Sep 2017 http://ceec.uk/ |
Conference
Conference | 9th Computer Science & Electronic Engineering Conference |
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Abbreviated title | CEEC'17 |
Country/Territory | United Kingdom |
City | Colchester |
Period | 27/09/17 → 29/09/17 |
Internet address |
Keywords
- MPLS
- GMPLS network optimization
- particle swarm optimization
- heurastic algorithm
- traffic engineering
- weight inertia