Machine Learning for Multiobjective Evolutionary Optimization in Python for EM Problems

A highly efficient algorithm for the optimization of EM problems is proposed. The algorithm is mostly based on Python open-source libraries and was successfully used in the development of antennas and arrays using HFSS.

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Bibliographic Details
Published inDigest - IEEE Antennas and Propagation Society. International Symposium (1995) pp. 541 - 542
Main Authors Boryssenko, Anatoliy, Herscovici, Naftali
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Antenna arrays
Computational modeling
Gaussian process
HFSS
Linear programming
Machine learning
Machine learning algorithms
multiobjective evolutionary algorithm (MOEA)
Optimization
Python
surrogate objective function
Online AccessGet full text
ISSN1947-1491
DOI10.1109/APUSNCURSINRSM.2018.8609394

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Summary:A highly efficient algorithm for the optimization of EM problems is proposed. The algorithm is mostly based on Python open-source libraries and was successfully used in the development of antennas and arrays using HFSS.
ISSN:1947-1491
DOI:10.1109/APUSNCURSINRSM.2018.8609394