Abstract
This paper presents a genetic-inspired multicast routing algorithm with Quality of Service (i.e., bandwidth and end-to-end delay) constraints. The aim is to efficiently discover a minimum-cost multicast tree (a set of paths) that satisfactorily helps various services from a designated source to multiple destinations. To achieve this goal, state of the art genetic-based optimization techniques are employed. Each chromosome is represented as a tree structure of Genetic Programming. A fitness function that returns a tree cost has been suggested. New variation operators (i.e., crossover and mutation) are designed in this regard. Crossover exchanges partial chromosomes (i.e., sub-trees) in a positionally independent manner. Mutation introduces (in part) a new sub-tree with low probability. Moreover, all the infeasible chromosomes are treated with a simple repair function. The synergy achieved by combing new ingredients (i.e., representation, crossover, and mutation) offers an effective search capability that results in improved quality of solution and enhanced rate of convergence. Experimental results show that the proposed GA achieves minimal spanning tree, fast convergence speed, and high reliability. Further, its performance is better than that of a comparative reference.
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Oh, S., Ahn, C., Ramakrishna, R.S. (2006). A Genetic-Inspired Multicast Routing Optimization Algorithm with Bandwidth and End-to-End Delay Constraints. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_89
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DOI: https://doi.org/10.1007/11893295_89
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