An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

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.

Implicit Label Number for Optical Packets in Label Switching Networks Based on Spectral Amplitude Coding and OCDM Path

“…6 shows the minimum of the labeled bits L versus the stacked label number k for M-sequence and stuffed quadratic congruence (SQC) codes. The parameters for both codes are respectively (N,,) = (15,8,4) and (13,4,1), where the sequences with similar codes lengths are selected for fair comparisons. The difference in the L values between M-sequence and SQC comes from the different noise levels raised in the decoding process.…”

“…ENERALIZED multiprotocol label switching (GMPLS) is a primary protocol for realizing optical packet switching (OPS), as it provides high efficiency in switching and bandwidth utilization with a simplified control plane [1,2]. A packet in GMPLS is routed along a label switch path (LSP), which is established by pairs of routers reading the packet label.…”

Decomposition-Based Multiobjective Evolutionary Algorithm With Genetically Hybrid Differential Evolution Strategy

“…To obtain a good approximation on PF for making decisions, multiobjective evolutionary algorithms (MOEAs) have been a popular approach to optimize various MOPs [9]- [11]. In recent decades, a variety of MOEAs have been developed to solve various theoretical optimization problems and even some real-world applications [12]- [16]. MOEAs are divided into several categories with different criteria in terms of the environmental selection: 1) Pareto-dominance-based framework [17]- [20], 2) indicator-based framework [21]- [23], and 3) decomposition-based framework [24]- [30].…”

Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

“…Some popular evolutionary algorithms, such as genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO) are widely applied to design advanced MLCs. These algorithms are already central in control design and provide context for modern MLCs [20][21][22][23][24].…”