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 |
|---|---|
| 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 |
|---|---|
| Abbreviated title | CEEC'17 |
| Country | United Kingdom |
| City | Colchester |
| Period | 27/09/17 → 29/09/17 |
| Internet address |
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Keywords
- MPLS
- GMPLS network optimization
- particle swarm optimization
- heurastic algorithm
- traffic engineering
- weight inertia
Cite this
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A Pareto based approach with elitist learning strategy for MPLS/GMPS networks. / Masood, Mohsin; Fouad, Mohamed Mostafa; Glesk, Ivan.
2017. 9th Computer Science & Electronic Engineering Conference, Colchester, United Kingdom.Research output: Contribution to conference › Proceeding
TY - CONF
T1 - A Pareto based approach with elitist learning strategy for MPLS/GMPS networks
AU - Masood, Mohsin
AU - Fouad, Mohamed Mostafa
AU - Glesk, Ivan
N1 - (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - 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.
AB - 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.
KW - MPLS
KW - GMPLS network optimization
KW - particle swarm optimization
KW - heurastic algorithm
KW - traffic engineering
KW - weight inertia
M3 - Proceeding
ER -