Referenced Items are not available for this document.
In this paper, we discuss multiobjective optimization problems solved by evolutionary algorithms. We present the nondominated sorting genetic algorithm (NSGA) to solve this class of problems and its performance is analyzed by comparing its results with those obtained with four other algorithms. Finally, the NSGA is applied to solve the TEAM benchmark problem 22 without considering the quench physical condition to map the Pareto-optimum front. The results in both analytical and electromagnetic problems show its effectiveness
Published in:
Magnetics, IEEE Transactions on
(Volume:38
,
Issue:
2
)
Date of Publication: Mar 2002
- Page(s):
- 1133 - 1136
- ISSN :
- 0018-9464
- INSPEC Accession Number:
- 7265147
- Digital Object Identifier :
- 10.1109/20.996290
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 07 August 2002
- Issue Date :
- Mar 2002
- Sponsored by :
- IEEE Magnetics Society
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