Abstract
Multiobjective optimization is a research area for which several evolutionary algorithms have been used. Multiobjective evolutionary algorithms (MOEAs) have successfully been used in a wide range of tasks from toy problems to realworld applications. Among several MOEAs, two categories are highlighted: the multiobjective approaches, which are able to deal with 2 and 3 objectives and, more recently, the manyobjective approaches, specially designed to deal with 4 or more objectives. In this paper, we investigate five MOEAs, two of them - SPEA2 and NSGA-II - are well-known multiobjective methods to deal with few objectives, while other two of them - NSGA-III and MEAMT - represent the class of many-objective approaches. The fifth algorithm is MOEA/D that can be seen as a transition between the multi and the many-objective approaches. The five MOEAs are applied here in two well-known discrete optimization problems: the multicast routing problem (MRP) and the multiobjective knapsack problem (MKP). The experimental results were used to analize the behavior of the MOEAs with respect to the number of objectives and the optimization problem.
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