A New Implementation of Population Based Incremental Learning Method for Optimizations in Electromagnetics

Author

Yang, S.Y. ; Ho, S.L. ; Ni, G.Z. ; Machado, José Márcio ; Wong, K.F.

Author_Institution

Zhejiang Univ., Hangzhou

Volume

43

Issue

4

fYear

2007

fDate

4/1/2007 12:00:00 AM

Firstpage

1601

Lastpage

1604

Abstract

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm

Keywords

computational electromagnetics; genetic algorithms; learning (artificial intelligence); adaptive updating strategy; electromagnetic optimization; global search ability; multiple probability vectors; mutation operators; population based incremental learning method; Algorithm design and analysis; Artificial neural networks; Biological cells; Educational institutions; Electromagnetics; Genetic mutations; Inverse problems; Learning systems; Optimization methods; Stochastic processes; Genetic algorithm (GA); global optimization; inverse problem; population based incremental learning (PBIL) method;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2006.892112

Filename

4137821