Routing algorithm with QoS constraints for real time multicast communications using particle swarm optimization and genetic meta-optimizer. Issue 2 (18th April 2016)

• Record Type: Journal Article
• Authors: CAMARGO, YEISON JULIAN; Ramirez, Leonardo Juan; Martinez, Ana Karina
• Is Part Of: Engineering computations. Volume 33:Issue 2(2016)
• Page Start: 344
• Page End: 365
• Publication Date: 2016-04-18
• Subjects: Optimization -- Routing -- Genetic algorithms -- Particle swarm optimization -- Multicast -- NP-complete -- Multicast communication; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-10-2014-0205
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: – The current work shows an approach to solve the quality of service (QoS) multicast routing problem by using particle swarm optimization (PSO). The problem of finding a route from a source node to multiple destination nodes (multicast) at a minimum cost is an NP-complete problem (Steiner tree problem) and is even greater if QoS constraints are taken into account. Thus, approximation algorithms are necessary to solve this problem. The purpose of this paper is to present a routing algorithm with two QoS constraints (delay and delay variation) for solving the routing problem based on a modified version of PSO. Design/methodology/approach: – This work involved the following methodology: Literature review; Routing algorithm design; Implementation of the designed routing algorithm by java programming; and Simulations and results. Findings: – In this work the authors compared the routing algorithm against the exhaustive search approach. The results showed that the algorithm improves the execution times in about 40 percent with different topologies. Research limitations/implications: – The algorithm was tested in three different topologies with 30, 40 and 50 nodes with and a dense graph topology. Originality/value: – The algorithm implements a novel technique for fine tuning the parameters of the implemented bio-inspired model (PSO) by using a genetic meta-optimizer. The authors also present a simple and multi implementation approach by using an encoding system that fits multiple bio-inspired models.