Optimizing Multiples Objectives in Dynamic Multicast Groups using a probabilistic BFS Algorithm

Generalized Multiobjective Multitree model (GMMmodel) considering 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 proposed. In this paper, we extends the GMM-model to dynamic multicast groups. If a multicast tree is recomputed from scratch, it may consume a considerable amount of CPU time and all communication using the multicast tree will be temporarily interrupted. To alleviate these drawbacks we propose a Dynamic Generalized Multiobjective Multitree model (D-GMM-model) that in order to add new egress nodes makes use of a multicast tree previously computed with GMM-model. To solve the Dynamic-GMM-model, a Dynamic-GMM algorithm (D-GMM) is proposed. Experimental results considering up to 11 different objectives are presented. We compare the GMM-model performance using MOEA with the proposed Dynamic- GMM-model using D-GMM. The main contributions are the optimization model for dynamic multicast routing; and the heuristic algorithm proposed with polynomial complexity.