DocumentCode
1275144
Title
Multiobjective genetic algorithms applied to solve optimization problems
Author
Dias, Alexandre H F ; De Vasconcelos, Jõao A.
Author_Institution
Acesita Co., Timoteo, Brazil
Volume
38
Issue
2
fYear
2002
fDate
3/1/2002 12:00:00 AM
Firstpage
1133
Lastpage
1136
Abstract
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
Keywords
electromagnetic field theory; genetic algorithms; sorting; NSGA; Pareto-optimum front mapping; TEAM benchmark problem; electromagnetic problems; evolutionary algorithms; multiobjective genetic algorithms; multiobjective optimization problems; nondominated sorting genetic algorithm; optimization problems; quench physical condition; Algorithm design and analysis; Electromagnetic analysis; Evolutionary computation; Genetic algorithms; Performance analysis; Performance evaluation; Sections; Sorting; Testing;
fLanguage
English
Journal_Title
Magnetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9464
Type
jour
DOI
10.1109/20.996290
Filename
996290
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