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Network coding is an emerging technique in communication networks, where the intennediate nodes are allowed to combine (code) the data received from different incoming links if necessary. The thesis investigates a nwnber of routing problems for network coding based multicast (NCM). which belong to combinatorial optimization problems (COPs). Evolutionary algorithms (EAs) are used to srudy the problems. The work of the thesis are described below. We propose three EAs for the network coding resource minimization (NCRM) problem where the objective is to minimize the number of coding operations while meeting the data rate requirement based on NCM. The three EAs are population based incremental learning (PBIL), compact genetic algorithm (cGA) and path-oriented encoding EA (PEA), all specially developed for tackling the NCRM problem. Ta support real-time multimedia applications, we for the first time extend the NCRM problem by introducing the maximum transmission delay into the problem as a constraint, which is called the delay constrained NCRM problem. Benchmark datasets are created based on the datasels for the NCRM problem. Three EAs originally used for the NCRM problem are adapted for the delay constrained NCRM ptoblem, including GAs and PBIL. To study the conflicting interests of service providers and network users, we for the first time fonnulate a multi-objective NCM routing problem considering two objectives, cost and delay. The cost is the summation of the coding cost and link cost incurred in the NCM. The delay is the maximum transmission delay of paths in the NCM. This problem is referred to as the cost-delay bi-objective optimization (CDBO) problem. Benchmark datasets for the delay constrained NCRM problem are used to generate the datasets for the CDBO problem. Elitist nondominated sorting GA (NSGA-II) is adapted for the CDBO problem. |
