Algorithms to Augment Diversity and Convergence in Multiobjective Multicast Flow Routing

• Published in: 2010 Eleventh Brazilian Symposium on Neural Networks pp. 158 - 163
• Main Authors: Bueno, M L P, Oliveira, G M B
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: Convergence; Delay; Evolutionary computation; evolutionary multiobjective optimization; genetic algorithm; Heuristic algorithms; multicast routing; path relinking; Quality of service; Routing; steiner tree problem
• ISBN: 1424483913; 9781424483914
• ISSN: 1522-4899
• DOI: 10.1109/SBRN.2010.35

Multicast transmission corresponds to send data to several destinations, often involving requirements of Quality of Service (QoS) and Traffic Engineering (TE). These multiple requirements lead to the need of optimizing a set of conflicting objectives subject to constraints. Starting from the well-known evolutionary algorithm SPEA2, two formulations for the Routing problem were considered, minimizing four objectives - maximum link utilization, total cost, maximum end-to-end delay and hops count - subject to a link capacity constraint. The key investigation performed here is about the incorporation of a mechanism to reduced repeated individuals along the population, evaluating three algorithms for such task. One of them (f tm ) is proposed on this study, whose results in six instances of the problem with five metrics to evaluate convergence and diversity goals, indicates that the evolutionary model based on SPEA2 using our algorithm had returned the better results.