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Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

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3 Author(s)
Bora, T.C. ; Grad. em Eng. de Controle e Automacao, Pontificia Univ. Catolica do Parana, Curitiba, Brazil ; Lebensztajn, L. ; Coelho, L.D.S.

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Published in:

Magnetics, IEEE Transactions on  (Volume:48 ,  Issue: 2 )