Journal
IEEE TRANSACTIONS ON MAGNETICS
Volume 48, Issue 2, Pages 767-770Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2011.2177076
Keywords
Evolutionary computation; optimization; satellite antennas
Funding
- National Council of Scientific and Technologic Development of Brazil-CNPq [303963/2009-3/PQ, 306151/2009-0/PQ, 478158/2009-3]
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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.
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