José Elias C. ArroyoVinícius A. Armentano

On-Line Neo-Fuzzy-Neuron State Observer

In this article, we propose a genetic algorithm to generate nondominated solutions of multiobjective combinatorial optimization problems. The algorithm is based on the concept of Pareto dominance and uses strategies for multiobjective optimization such as: elitism, population classification and diversification, and intensification by local search. The method is tested on the traveling salesman problem with two objectives. For problems involving at most 12 cities the solutions obtained by the algorithm are compared to efficient solutions. For larger problems the algorithm is compared with two genetic algorithms presented in the literature. Computational tests show the good performance of the proposed algorithm. Clique no link abaixo para buscar o texto completo deste trabalho na Web: Buscar na Web

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