EMODS: A NOVEL EVOLUTIONARY METAHEURISTIC BASED IN THE AUTOMATA THEORY FOR THE MULTIOBJECTIVE OPTIMIZATION OF COMBINATORIALS PROBLEMS

摘要

This paper states a novel Evolutionary Metaheuristic based in the Automata Theory for the Multiobjective Optimization of Combinatorial Problems named EMODS. The proposed algorithm uses the natural selection theory to explore the feasible solutions space of a Combinatorial Problem. Due to this, local optimums are avoided. Also, EMODS takes advantage in the optimization process from the Metaheuristic of Deterministic Swapping to avoid finding unfeasible solutions. The proposed algorithm was tested using well known instances from the TSPLIB with three objectives. Its results were compared against four Multiobjective Simulated Annealing inspired Algorithms using metrics from the specialized literature. In every case, the EMODS results on the metrics were always better and in some of those cases, the distance from the Real Solutions was 4%.