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Genetic algorithms in bidisciplinary (aerodynamics/electromagnetism) optimization

Zhu, Ziqiang; Li, Haiming; Li, Jin and Yu, Rixin LU (2001) In Science in China, Series E: Technological Sciences 44(6). p.572-580
Abstract

The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced. Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.

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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Deterministic optimization, Genetic algorithm, Multidisciplinary optimization
in
Science in China, Series E: Technological Sciences
volume
44
issue
6
pages
9 pages
publisher
Science in China Press
external identifiers
  • scopus:0041903570
ISSN
1006-9321
language
English
LU publication?
no
id
d723d01b-1743-413b-8cbf-29a0d425e9fb
date added to LUP
2019-09-13 13:06:09
date last changed
2019-11-05 05:25:58
@article{d723d01b-1743-413b-8cbf-29a0d425e9fb,
  abstract     = {<p>The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced. Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.</p>},
  author       = {Zhu, Ziqiang and Li, Haiming and Li, Jin and Yu, Rixin},
  issn         = {1006-9321},
  keyword      = {Deterministic optimization,Genetic algorithm,Multidisciplinary optimization},
  language     = {eng},
  month        = {12},
  number       = {6},
  pages        = {572--580},
  publisher    = {Science in China Press},
  series       = {Science in China, Series E: Technological Sciences},
  title        = {Genetic algorithms in bidisciplinary (aerodynamics/electromagnetism) optimization},
  volume       = {44},
  year         = {2001},
}