Authors:
Valery Katerinchuk
1
;
Andreas Albrecht
2
and
Kathleen Steinhöfel
1
Affiliations:
1
King’s College London, United Kingdom
;
2
Middlesex University London, United Kingdom
Keyword(s):
Energy Efficiency, Wired Networks, Multiple Multicast Requests, Multicast Routing, Simulated Annealing, Genetic Algorithm, Hybrid Algorithm, Partially Mixed Crossover.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Soft Computing
Abstract:
Energy-efficient multicast routing algorithms have predominantly focused on wireless or ad-hoc mobile networks. However, since the turn of the century the need for energy efficient approaches to routing in wired networks has been steadily rising. In this paper, we introduce an objective function for multicast routing in wired networks taking energy consumption into consideration. A number of hybrid Genetic and Simulated Annealing based algorithms have been shown to be able to find better solutions to the multicast routing problem compared to solely Genetic or Simulated Annealing based algorithms. Our approach adapts a population-based hybrid algorithm for routing multiple simultaneous multicast requests. We examine the performance in terms of energy efficiency against solutions found by Logarithmic Simulated Annealing and Genetic based algorithms. We find that the hybrid approach, in 87% of instances, was able to find superior solutions, and in 96% of instances, solutions superior or
equal to the best solution given by either Simulated Annealing or Genetic approaches. The extent of the improvement however varied greatly from a few hundred to within ten Joules, with the improvement on the best solution ranging from 5.6 to 531.5 Joules.
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