A Dynamic Multiobjective Evolutionary Algorithm for Multicast Routing Problem

In this work, we propose an evolutionary algorithm to tackle a multiobjective optimization problem, namely a constrained multicast routing with quality demands. The proposed algorithm embeds two different strategies along with SPEA2 (Strength Pareto Evolutionary Algorithm 2) method attempting to improve convergence to Pareto front. These strategies are a heuristic for population diversity augmentation and a neighborhood mating selection scheme. Experimental results showed that selecting which strategy to use depends on population dynamics aspects described by non dominated solutions over evolutionary iterations. It was possible to observe that the proposed mechanism can help the algorithm to achieve better solutions over convergence and diversity goals in most cases.