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.
Highlights
I N a network terminology, network efficiency term is used for the effective use of the network resources
This paper offers an adapted version of Particle swarm optimization (PSO) algorithm for solving both local and global optima problem with a fast convergence rate for the Multi-Constrained Optimal Path (MCOP) optimization problem in Multi-Protocol Label Switched (MPLS)/ GMPLS networks
While the global best version of particle swarm optimization (Gbest) model is suitable for uni-modal optimization problems, but can be trapped into local optima for multi-modals problems
Summary
I N a network terminology, network efficiency term is used for the effective use of the network resources. Pareto based solutions consist of Pareto set (multiple feasible solutions) in the multi-objective spaces These optimization challenges can be solved either by exact or approximate/ meta-heuristic approaches. Multiple versions had been proposed, including hybrid and adaptive models of PSO to rescue algorithm's local and/or global optima problem [6]–[8]. This paper offers an adapted version of PSO algorithm for solving both local and global optima problem with a fast convergence rate for the MCOP optimization problem in MPLS/ GMPLS networks. The rest of the paper is organized as follows; Section 2 represents an importance of meta-heuristic algorithms including PSO algorithm for solving various optimization problems and discusses the related work to PSO algorithm, subject to its various improved versions. For MPLS and GMPLS networks MPLS/ GMPLS keywords are used
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