DocumentCode
2915053
Title
Evolutionary many-objective optimization: A short review
Author
Ishibuchi, Hisao ; Tsukamoto, Noritaka ; Nojima, Yusuke
Author_Institution
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai
fYear
2008
fDate
1-6 June 2008
Firstpage
2419
Lastpage
2426
Abstract
Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been used in a wide range of real-world application tasks, difficulties in their scalability to many-objective problems have also been reported. In this paper, first we demonstrate those difficulties through computational experiments. Then we review some approaches proposed in the literature for the scalability improvement of EMO algorithms. Finally we suggest future research directions in evolutionary many-objective optimization.
Keywords
evolutionary computation; optimisation; EMO algorithms; evolutionary many-objective optimization; multiobjective optimization; Scalability;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-1822-0
Electronic_ISBN
978-1-4244-1823-7
Type
conf
DOI
10.1109/CEC.2008.4631121
Filename
4631121
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