Swarm Intelligence Optimization Algorithm In The QoS Multicast Routing In Applied Study

With widely application of the rapid development of network technology, especially multimedia technologies (such as video conferencing, Internet TV, online gaming, IP phones, and so on), higher demands of the transmission capacity of the network set are required. Such applications often require a network which has the ability of multicast, and multicast communication is designed for multicast and multi-collaborative communication, this communication method has only one copy of the data on the main link, replication transport of packets are done by routers on the way of the branch chain, which greatly reduces the consumption of network resources and improves the transfer efficiency. Multicast communications obviously will become one of the most important supporting technologies among newer network data transmission technologies. Multicast routing is the key and core part of multicast communication. To implement the multicast communication, the multicast path must be identified in advance, multicast path is described by multicast distribution tree and multicast routers will build the multicast distribution tree.QOS (quality of service) can ensure the efficient transmission of network traffic and provides a variety of service quality corresponding to the various needs for all network services separately to achieve the purpose of distinguishing different services and improving service quality.An important task of QoS multicast router is to build a multicast tree which can meet demands for all kinds of services, Kompella previously proved that it’s a NP-Complete problem for a QOS multicast router which has two or more unrelated additivity, and proposed the KPP algorithm to construct a Steiner tree to meet the delayed constraint. With the in-depth development of multimedia technology, multimedia service requests higher demand on network resources, hence multiple constrained QoS multicast routing technology becomes a quite hot topic.Modern intelligent optimization is based on swarm intelligence exhibited by natural biological communities arising from the nature, the algorithm is designed to be simple, easy to be understood and without special requirements in the field of the application, therefore study and application of swarm intelligence optimization algorithm is widely implemented.Traditional QOS group broadcast routing algorithm (shortest path tree algorithm, and minimum Steiner tree algorithm) has special mathematical model and strictly demonstration of their own, however, the network structure in reality often is complex and uncertain and can not be described by a single model. While group smart algorithm is a heuristic algorithm and does not need to establish an accurate model, it can seek out viable solutions for complex networks that have two or more targets and constraints.In recent years, a couple of articles which are about applying genetic algorithm, Ant group algorithm, particles group algorithm and smart group optimization algorithm to QoS group broadcast routing have been published, these articles mainly focused on time of convergence, implementing efficiency and capacity of search for exploration. Since each article adopt its own simulation model which is different from others, we cannot make comparison between those algorithms and formed a full awareness.In this paper, we establish a topology model for a random network using Salama random network topology generation algorithm in which we define identical source nodes, multicast group nodes and QOS constraints in the same network topology model based on genetic algorithms, Ant Colony Optimization, Particle Swarm Optimization, Firefly algorithm for QOS multicast trees and the study of QOS related parameters. This simulation was implemented on a Matlab simulation platform, the chart and analysis of the simulation results were also done on the same platform as well.