Generalized multiobjective multitree model for dynamic multicast groups

Generalized multiobjective multitree model (GMM-model) studied for the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the strength Pareto evolutionary algorithm (SPEA) was already proposed. In this paper, we extend the GMM-model to dynamic multicast groups (i.e. egress nodes can change during the connection´s lifetime), given that, if recomputed from scratch, it may consume a considerable amount of CPU time. To alleviate this drawback we propose a dynamic generalized multiobjective multitree model (dynamic-GMM-model) that in order to add new egress nodes makes use of a multicast tree previously computed with the GMM-model. To solve the dynamic-GMM-model, a new MASPA (multiobjective approximation using shortest path algorithm) heuristic is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network. We compare the performance of the GMM-model using MOEA with the proposed dynamic-GMM-model using MASPA, showing that reasonable good solutions may be found using fewer resources (such as memory and time). The main contributions of this paper are the optimization model for dynamic multicast routing; and the proposed heuristic algorithm.