NDLI: Solving multiobjective multicast routing problem with a new ant colony optimization approach

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Content Provider	ACM Digital Library
Author	Pinto, Diego Barán, Benjamín
Abstract	This work presents two multiobjective algorithms for Multicast Traffic Engineering. The proposed algorithms are new versions of the Multi-Objective Ant Colony System (MOACS) and the Max-Min Ant System (MMAS), based on Ant Colony Optimization (ACO). Both ACO algorithms simultaneously optimize maximum link utilization and cost of a multicast routing tree, as well as average delay and maximum end-to-end delay, for the first time using an ACO approach. In this way, a set of optimal solutions, know as Pareto set is calculated in only one run of the algorithms, without a priori restrictions. Experimental results show a promising performance of both proposed algorithms for a multicast traffic engineering optimization, when compared to a recently published Multiobjective Multicast Algorithm (MMA), specially designed for Multiobjective Multicast Routing Problems.
Starting Page	11
Ending Page	19
Page Count	9
File Format	PDF
ISBN	1595930086
DOI	10.1145/1168117.1168120
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-10-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ant colony optimization Multiobjective optimization Traffic engineering Multicast routing
Content Type	Text
Resource Type	Article

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Solving multiobjective multicast routing problem with a new ant colony optimization approach