DocumentCode
1863602
Title
An improved density estimation method in NSGA2
Author
Li Huiyuan ; Su Yixin
Author_Institution
Department of Automation, Wuhan University of Technology, China
fYear
2012
fDate
3-5 March 2012
Firstpage
429
Lastpage
432
Abstract
Multi-Objective Evolutionary Algorithms (MOEAs) are efficient and widely accepted ways in solving Multi- Objective Problems (MOPs), and NSGA2 may be the most popular one. Diversity distribution is one of the major indexes to reflect the performance of MOEAs. The diversity maintenance strategy in NSGA2, is a density estimation method, called crowding-distance also, based on which, a new density estimation method is proposed in this paper. The new method has been tested with five test problems, and it performs better in diversity preservation.
Keywords
MOEAs; NSGA2; crowding-distance; density estimation; diversity distribution;
fLanguage
English
Publisher
iet
Conference_Titel
Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on
Conference_Location
Xiamen
Electronic_ISBN
978-1-84919-537-9
Type
conf
DOI
10.1049/cp.2012.1008
Filename
6492615
Link To Document