Power economic dispatch using a hybrid genetic algorithm

Author

Yalcinoz, T. ; Altun, H.

Author_Institution

Dept. of Electr. & Electron. Eng., Nigde Univ., Turkey

Volume

21

Issue

3

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

59

Lastpage

60

Abstract

This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) as a part of its initial population. Elitism, arithmetic crossover and mutation are used in the GAs to generate successive sets of possible operating policies. The technique improves the quality of the solution and reduces the computation time, and is compared with the classical optimization technique, an improved Hopfield NN approach (IHN), a fuzzy logic controlled GA and an improved GA

Keywords

Hopfield neural nets; genetic algorithms; power generation dispatch; power generation economics; power generation planning; power system analysis computing; arithmetic crossover; computation time; elitism; hybrid genetic algorithm; improved Hopfield neural network; initial population; mutation; possible operating policies; power economic dispatch; Arithmetic; Biological cells; Environmental economics; Fuzzy logic; Genetic algorithms; Genetic mutations; Hopfield neural networks; Neural networks; Power generation economics; Power system economics;

fLanguage

English

Journal_Title

Power Engineering Review, IEEE

Publisher

ieee

ISSN

0272-1724

Type

jour

DOI

10.1109/39.911360

Filename

911360