A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 23; no. 5; pp. 748 - 761
Main Authors:	Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Convergence Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Many-objective evolutionary optimization algorithm Multiple objective analysis nadir point Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Performance degradation Performance enhancement Performance evaluation Sociology Statistics Sun two-stage method
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!