Multi Mixed Population Evolutionary Algorithm applied to the Multiobjective Degree Constrained Minimum Spanning Tree Problem
Genetic Algorithms, Hybrid Genetic Algorithms, Multiobjective Programming, Degree Constrained Minimum Spanning Tree.
In recent years, the Multiobjective Degree Constrained Minimum Spanning Tree Problem, has taken the attention of combinatorial optimization researchers, especially due to its wide usability in practical network modeling design problems. This is a NP-hard problem, even in its mono-objective version for a degree of at least . The new algorithm proposed here called MMPEA, uses shared external archives and different multiobjective optimization techniques in a parallel execution to a better survey of the search space. This MMPEA version adopts the MPAES, NSGA2 and SPEA2 algorithms in its implementation which also are used in the comparison tests. A total of 5040 empirical tests are presented here, including different graph generators, and instances of size 50 up to 1000 vertices. For a matter of multi-objective trait, the results for these experiments are presented by means of hypervolume and -binary indicators. The significance of computational experiments is evaluated by the Mann-Whitney statistical test.