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

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 met...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on magnetics 2012-02, Vol.48 (2), p.767-770
Main Authors: Bora, Teodoro C., Lebensztajn, Luiz, Coelho, Leandro Dos S.
Format: Article
Language:English
Subjects:
Broadband antennas
Cross-disciplinary physics: materials science
rheology
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Learning
Materials science
Optimization
Other topics in materials science
Physics
Satellite antennas
Satellites
Sorting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2011.2177076