DocumentCode
2751968
Title
Variants of Differential Evolution for Multi-Objective Optimization
Author
Zielinski, Karin ; Laur, Rainer
Author_Institution
Inst. for Electromagn. Theor. & Microelectron., Bremen Univ.
fYear
2007
fDate
1-5 April 2007
Firstpage
91
Lastpage
98
Abstract
In multi-objective optimization not only fast convergence is important, but it is also necessary to keep enough diversity so that the whole Pareto-optimal front can be found. In this work four variants of differential evolution are examined that differ in the selection scheme and in the assignment of crowding distance. The assumption is checked that the variants differ in convergence speed and amount of diversity. The performance is shown for 1000 consecutive generations, so that different behavior over time can be detected
Keywords
Pareto optimisation; convergence; operations research; Pareto-optimal front; convergence speed; crowding distance assignment; differential evolution; multiobjective optimization; Chromium; Computational intelligence; Constraint optimization; Convergence; Decision making; Electric variables control; Genetic mutations; Microelectronics; Random variables; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0702-8
Type
conf
DOI
10.1109/MCDM.2007.369422
Filename
4222988
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