Construction of integral objective function/fitness function of multi-objective/multi-disciplinary optimization
(2004) In CMES - Computer Modeling in Engineering and Sciences 6(6). p.567-576- Abstract
To extend an available mono-objective optimization method to multi-objective/multi-disciplinary optimization, the construction of a suitable integral objective function (in gradient based deterministic method-DM) or fitness function (in genetic algorithm-GA) is important. An auto-adjusting weighted object optimization (AWO) method in DM is suggested to improve the available weighted sum method (linear combined weighted object optimizationLWO method). Two formulae of fitness function in GA are suggested for two kinds of design problems. Flow field solution is obtained by solving Euler equations. Electromagnetic field solution is obtained by solving Maxwell equations. Bi-disciplinary optimization computation is carried out by coupling... (More)
To extend an available mono-objective optimization method to multi-objective/multi-disciplinary optimization, the construction of a suitable integral objective function (in gradient based deterministic method-DM) or fitness function (in genetic algorithm-GA) is important. An auto-adjusting weighted object optimization (AWO) method in DM is suggested to improve the available weighted sum method (linear combined weighted object optimizationLWO method). Two formulae of fitness function in GA are suggested for two kinds of design problems. Flow field solution is obtained by solving Euler equations. Electromagnetic field solution is obtained by solving Maxwell equations. Bi-disciplinary optimization computation is carried out by coupling these two solutions with a nonlinear optimization method. Numerical results show that the needed Pareto solutions can be effectively obtained by using these suggested methods to meet the original design requirements.
(Less)
- author
- Zhu, Z. Q. ; Liu, Z. ; Wang, X. L. and Yu, R. X. LU
- publishing date
- 2004-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Euler equations, Genetic algorithms, Maxwell equations, Multiobjective/multidisciplinary optimization
- in
- CMES - Computer Modeling in Engineering and Sciences
- volume
- 6
- issue
- 6
- pages
- 10 pages
- publisher
- Tech Science Press
- external identifiers
-
- scopus:11144290779
- ISSN
- 1526-1492
- language
- English
- LU publication?
- no
- id
- 27517759-e5a2-42d0-b78b-14e239daf6d5
- date added to LUP
- 2019-09-13 13:04:50
- date last changed
- 2022-02-01 00:36:01
@article{27517759-e5a2-42d0-b78b-14e239daf6d5,
abstract = {{<p>To extend an available mono-objective optimization method to multi-objective/multi-disciplinary optimization, the construction of a suitable integral objective function (in gradient based deterministic method-DM) or fitness function (in genetic algorithm-GA) is important. An auto-adjusting weighted object optimization (AWO) method in DM is suggested to improve the available weighted sum method (linear combined weighted object optimizationLWO method). Two formulae of fitness function in GA are suggested for two kinds of design problems. Flow field solution is obtained by solving Euler equations. Electromagnetic field solution is obtained by solving Maxwell equations. Bi-disciplinary optimization computation is carried out by coupling these two solutions with a nonlinear optimization method. Numerical results show that the needed Pareto solutions can be effectively obtained by using these suggested methods to meet the original design requirements.</p>}},
author = {{Zhu, Z. Q. and Liu, Z. and Wang, X. L. and Yu, R. X.}},
issn = {{1526-1492}},
keywords = {{Euler equations; Genetic algorithms; Maxwell equations; Multiobjective/multidisciplinary optimization}},
language = {{eng}},
month = {{12}},
number = {{6}},
pages = {{567--576}},
publisher = {{Tech Science Press}},
series = {{CMES - Computer Modeling in Engineering and Sciences}},
title = {{Construction of integral objective function/fitness function of multi-objective/multi-disciplinary optimization}},
volume = {{6}},
year = {{2004}},
}