Four-objective formulations of multicast flows via evolutionary algorithms with quality demands
In this work, we investigate two four-objective formulations of multicast routing problem, in which a tree must be set to deliver data to a subset of destination nodes in a network, optimizing several conflicting objectives. We propose a routing model based on SPEA2 (Strength Pareto Evolutionary Algorithm2) to handle it, incorporating a heuristic that performs a reconnection step in crossover and mutation operators in order to produce a new tree. Three different heuristics were designed for such step. Experimental results were conducted to assess convergence and diversity goals over well-known instances of the problem, showing that the heuristic which alternates between shortest path and randomness produced the best results on most cases. It was shown that the proposed model compares well with traditional algorithms, namely, Dijkstras algorithm and Takahashi-Matsuyama heuristic.
multicast routing heuristics traffic engineering multiobjective optimization genetic algorithm