Genetic algorithms in bidisciplinary (aerodynamics/electromagnetism) optimization
(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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d723d01b-1743-413b-8cbf-29a0d425e9fb
- author
- Zhu, Ziqiang ; Li, Haiming ; Li, Jin and Yu, Rixin LU
- publishing date
- 2001-12-01
- 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
- 2022-02-01 00:36:01
@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}}, keywords = {{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}}, }