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Constrained multicast routing optimi...

Al-Sharhan, Salah.

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Constrained multicast routing optimization using soft computing approaches.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Constrained multicast routing optimization using soft computing approaches./
Author:	Al-Sharhan, Salah.
Description:	151 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0941.
Contained By:	Dissertation Abstracts International64-02B.
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ77217
ISBN:	0612772179

Constrained multicast routing optimization using soft computing approaches.
Al-Sharhan, Salah.

Constrained multicast routing optimization using soft computing approaches. - 151 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0941.

Thesis (Ph.D.)--University of Waterloo (Canada), 2002.

Recently, a new era of networking has emerged and has introduced dramatic and rapid changes to the infrastructure of high-speed large-scale networks. The rapid growth witnessed recently in network technology, infrastructure, and services offered has created new challenges for telecommunication networks.

ISBN: 0612772179Subjects--Topical Terms:

1018128
Engineering, System Science.

Constrained multicast routing optimization using soft computing approaches.
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245 1 0 $a Constrained multicast routing optimization using soft computing approaches.
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0941.
500 $a Adviser: F. Karray.
502 $a Thesis (Ph.D.)--University of Waterloo (Canada), 2002.
520 $a Recently, a new era of networking has emerged and has introduced dramatic and rapid changes to the infrastructure of high-speed large-scale networks. The rapid growth witnessed recently in network technology, infrastructure, and services offered has created new challenges for telecommunication networks.
520 $a The design of high-speed networks involves a complex decision-making process about the type of switching mode, the switches position and their configuration, the link topology and sizing, the routing mechanisms, and the capacity assignment. An efficient technique is thus required to tackle this delicate and very important problem. Computational intelligence techniques based on tools of fuzzy logic, artificial neural networks, and evolutionary algorithms, have recently become among the state of the art techniques in the domain of artificial intelligence and expert systems design. They have attracted much interest in the last few years given their high potential in dealing effectively with large structural and parametric uncertainties of complex systems. The appropriate integrations of these tools can make them very powerful and highly efficient when applied to ill-defined systems, characterized by complex modeling and unpredicted behavior. In recent years a novel integration scheme involving genetic algorithms and competitive learning has led to a new paradigm: population-based incremental learning (PBIL). PBIL is an algorithm that integrates in a very efficient manner the features of genetic algorithms and those of competitive learning.
520 $a The main thrust of this research work is on developing a set of intelligent algorithms with main task of tackling the complex issues of Quality of Service Multicast Routing (QoSMCR). In today's Internet and wide area networks, QoS multicast routing becomes of vital importance. This is due to the fact that the Internet is rapidly becoming the main carrier of new multimedia applications and it is expected to play a significant role in the future deployment of these applications. The traffic generated by such applications is characterized by new requirements and performance measurements. Multimedia applications are usually resource intensive, have stringent quality of service requirements, and in many cases involve large multicast groups. Any multicast routing algorithm must satisfy these requirements. PBIL QoSMCR based algorithm is shown here to be an excellent candidate to tackle these issues effectively in terms of the cost and performance.
590 $a School code: 1141.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Computer Science. $3 626642
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710 2 0 $a University of Waterloo (Canada). $3 1017669
773 0 $t Dissertation Abstracts International $g 64-02B.
790 1 0 $a Karray, F., $e advisor
790 $a 1141
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ77217