An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks
File(s)
Published version
Author(s)
Masood, Mohsin
Fouad, Mohamed Mostafa
Kamal, Rashid
Glesk, Ivan
Khan, Imran Ullah
Type
Journal Article
Abstract
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.
Date Issued
2019-08-13
Date Acceptance
2019-07-21
Citation
IEEE Access, 2019, 7, pp.137147-137162
ISSN
2169-3536
Publisher
IEEE
Start Page
137147
End Page
137162
Journal / Book Title
IEEE Access
Volume
7
Copyright Statement
© 2019 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
License URL
Identifier
http://dx.doi.org/10.1109/access.2019.2934946
Publication Status
Published
Date Publish Online
2019-08-13