QoS Multicast Routing Based on Particle Swarm Optimization

2006 ◽  
pp. 936-943 ◽  
Author(s):  
Jing Liu ◽  
Jun Sun ◽  
Wenbo Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Multicast Routing ◽  
Particle Swarm ◽  
Swarm Optimization

A tree-based particle swarm optimization for multicast routing

2010 ◽  
Vol 54 (15) ◽  
pp. 2775-2786 ◽  
Author(s):  
Hua Wang ◽  
Xiangxu Meng ◽  
Shuai Li ◽  
Hong Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Multicast Routing ◽  
Particle Swarm ◽  
Swarm Optimization

Routing algorithm with QoS constraints for real time multicast communications using particle swarm optimization and genetic meta-optimizer

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
YEISON JULIAN CAMARGO ◽  
Leonardo Juan Ramirez ◽  
Ana Karina Martinez
Keyword(s):  
Particle Swarm Optimization ◽  
Design Methodology ◽  
Multicast Routing ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Fine Tuning ◽  
Steiner Tree Problem ◽  
Swarm Optimization ◽  
Routing Problem

Purpose The current work shows an approach to solve the 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 Quality of Service -QoS- constraints are taken into account. Thus, approximation algorithms are necessary to solve this problem. This work presents a routing algorithm with two QoS constraints (delay and delay variation) for solving the routing problem based on a modified version of particle swarm optimization. Design/methodology/approach This work involved the following methodology: 1. Literature Review 2. Routing algorithm design 3. Implementation of the designed routing algorithm by java programming. 4. Simulations and results. Findings In this work we compared our routing algorithm against the exhaustive search approach. The results showed that our algorithm improves the execution times in about 40% 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 Our algorithm implements a novel technique for fine tuning the parameters of the implemented bio-inspired model (Particles Swarm Optimization) by using a Genetic Meta-Optimizer. We also present a simple and multi implementation approach by using an encoding system that fits multiple bio-inspired models.

scholarly journals Energy-aware multicast routing in manet based on particle swarm optimization

2012 ◽  
Vol 1 ◽  
pp. 434-438 ◽  
Author(s):  
Alireza Sajedi Nasab ◽  
Vali Derhami ◽  
Leyli Mohammad Khanli ◽  
Ali Mohammad Zareha Bidoki
Keyword(s):  
Particle Swarm Optimization ◽  
Multicast Routing ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization
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