A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections

Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the searc...

Full description

Bibliographic Details

Published in:	IEEE transactions on evolutionary computation Vol. 19; no. 4; pp. 592 - 605
Main Authors:	Jixiang Cheng, Yen, Gary G., Gexiang Zhang
Format:	Journal Article
Language:	English
Published:	IEEE 01.08.2015
Subjects:	Algorithm design and analysis Convergence directional diversity Evolutionary computation favorable convergence Maintenance engineering many- objective evolutionary algorithm Many-objective optimization problem Optimization Sociology Statistics many-objective optimization problem (MaOP) many-objective evolutionary algorithm (MaOEA) Directional diversity (DD) favorable convergence (FC)
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!