Optimal multicast routing using genetic algorithm for WDM optical networks

We consider the multicast routing problem for large-scale wavelength division multiplexing (WDM) optical networks where transmission re-quests are established by point-to-multipoint connections. To realize multicast routing in WDM optical networks, some nodes need to havelight (optical) splitting capability. A node with splitting capability can forward an incoming message to more than one output link. We con-sider the problem of minimizing the number of split-capable nodes in the network for a given set of multicast requests. The maximum number of wavelengths that can be used is specified a priori. A genetic algorithm is proposed that exploits the combination of alternative shortest paths for the given multicast requests. This algorithm is examined for two realis-tic networks constructed based on the locations of major cities in Ibaraki Prefecture and those in Kanto District in Japan. Our experimental re-sults show that the proposed algorithm can reduce more than 10% of split-capable nodes compared with the case where the split-capable node placement optimization is not performed while the specified number of wavelengths is not exceeded.Includes bibliographical reference

A path-oriented encoding evolutionary algorithm for network coding resource minimization

Network coding is an emerging telecommunication technique, where any intermediate node is allowed to recombine incoming data if necessary. This technique helps to increase the throughput, however, very likely at the cost of huge amount of computational overhead, due to the packet recombination performed (ie coding operations). Hence, it is of practical importance to reduce coding operations while retaining the benefits that network coding brings to us. In this paper, we propose a novel evolutionary algorithm (EA) to minimize the amount of coding operations involved. Different from the state-of-the-art EAs which all use binary encodings for the problem, our EA is based on path-oriented encoding. In this new encoding scheme, each chromosome is represented by a union of paths originating from the source and terminating at one of the receivers. Employing path-oriented encoding leads to a search space where all solutions are feasible, which fundamentally facilitates more efficient search of EAs. Based on the new encoding, we develop three basic operators, that is, initialization, crossover and mutation. In addition, we design a local search operator to improve the solution quality and hence the performance of our EA. The simulation results demonstrate that our EA significantly outperforms the state-of-the-art algorithms in terms of global exploration and computational time