A new hybrid genetic algorithm and its application to the temperature neural network prediction in TFIH

Author

Chen, Tanggong ; Wang, Youhua ; Pang, Lingling ; Sun, Jingfeng ; An, Jinlong

Author_Institution

Province-Minist. Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin

fYear

2008

Firstpage

1

Lastpage

4

Abstract

Based on the analysis of the characters of genetic algorithm (GA) and particle swarm optimization (PSO), a new hybrid genetic algorithm is presented. This method integrates the well-known GA with PSO by embedding particle swarm operator into GA, and is applied to the temperature neural network (NN) prediction in transverse flux induction heating (TFIH). The results show that the performance of this algorithm is better than that of GA or PSO.

Keywords

genetic algorithms; induction heating; neural nets; particle swarm optimisation; power engineering computing; hybrid genetic algorithm; particle swarm optimization; temperature neural network prediction; transverse flux induction heating; Algorithm design and analysis; Computational modeling; Electromagnetic fields; Evolutionary computation; Genetic algorithms; Genetic mutations; Neural networks; Particle swarm optimization; Particle tracking; Temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation Congress, 2008. WAC 2008. World

Conference_Location

Hawaii, HI

Print_ISBN

978-1-889335-38-4

Electronic_ISBN

978-1-889335-37-7

Type

conf

Filename

4699137