A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance...

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Bibliographic Details

Published in:	IEEE transactions on evolutionary computation Vol. 23; no. 2; pp. 331 - 345
Main Authors:	Tian, Ye, Cheng, Ran, Zhang, Xingyi, Su, Yansen, Jin, Yaochu
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Balancing Convergence diversity Dominance evolutionary algorithm Evolutionary algorithms Evolutionary computation Genetic algorithms many-objective optimization Multiple objective analysis Optimization Pareto dominance Pareto optimization Production scheduling
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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