An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks
Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same ti...
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doaj-c9f56b0bd7424f03b53bcd65161b35812021-03-29T23:07:28ZengIEEEIEEE Access2169-35362019-01-01713714713716210.1109/ACCESS.2019.29349468795503An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS NetworksMohsin Masood0Mohamed Mostafa Fouad1Rashid Kamal2https://orcid.org/0000-0003-4388-569XIvan Glesk3https://orcid.org/0000-0002-3176-8069Imran Ullah Khan4Electronics and Electrical Engineering Department, University of Strathclyde, Glasgow, U.K.Arab Academy for Science, Technology & Maritime Transport, Cairo, EgyptDepartment of Computing and Technology, Abasyn University, Peshawar, PakistanElectronics and Electrical Engineering Department, University of Strathclyde, Glasgow, U.K.College of Underwater Acoustic Communication Engineering, Harbin Engineering University, Harbin, ChinaParticle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm.https://ieeexplore.ieee.org/document/8795503/Communication networks optimizationexploration problemmulti-objective constrained optimizationparticle swarm optimizationswarm intelligence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohsin Masood Mohamed Mostafa Fouad Rashid Kamal Ivan Glesk Imran Ullah Khan |
spellingShingle |
Mohsin Masood Mohamed Mostafa Fouad Rashid Kamal Ivan Glesk Imran Ullah Khan An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks IEEE Access Communication networks optimization exploration problem multi-objective constrained optimization particle swarm optimization swarm intelligence |
author_facet |
Mohsin Masood Mohamed Mostafa Fouad Rashid Kamal Ivan Glesk Imran Ullah Khan |
author_sort |
Mohsin Masood |
title |
An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks |
title_short |
An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks |
title_full |
An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks |
title_fullStr |
An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks |
title_full_unstemmed |
An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks |
title_sort |
improved particle swarm algorithm for multi-objectives based optimization in mpls/gmpls networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm. |
topic |
Communication networks optimization exploration problem multi-objective constrained optimization particle swarm optimization swarm intelligence |
url |
https://ieeexplore.ieee.org/document/8795503/ |
work_keys_str_mv |
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