University of Surrey

Test tubes in the lab Research in the ATI Dance Research

An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization

Zhang, X, Tian, Y, Cheng, R and Jin, Y (2015) An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 19 (2). pp. 201-213.

[img]
Preview
Text
ENS.pdf - ["content_typename_Submitted version (pre-print)" not defined]
Available under License : See the attached licence file.

Download (1MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

Evolutionary algorithms have been shown to be powerful for solving multiobjective optimization problems, in which nondominated sorting is a widely adopted technique in selection. This technique, however, can be computationally expensive, especially when the number of individuals in the population becomes large. This is mainly because in most existing nondominated sorting algorithms, a solution needs to be compared with all other solutions before it can be assigned to a front. In this paper we propose a novel, computationally efficient approach to nondominated sorting, termed efficient nondominated sort (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those that have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based on this new approach, two nondominated sorting algorithms have been suggested. Both theoretical analysis and empirical results show that the ENS-based sorting algorithms are computationally more efficient than the state-of-the-art nondominated sorting methods.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Zhang, XUNSPECIFIEDUNSPECIFIED
Tian, YUNSPECIFIEDUNSPECIFIED
Cheng, RUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 1 April 2015
Identification Number : 10.1109/TEVC.2014.2308305
Uncontrolled Keywords : Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science, Computational complexity, evolutionary multiobjective optimization, nondominated sorting, Pareto-optimality, NSGA-II, ALGORITHMS
Related URLs :
Additional Information : Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubspermissions@ieee.org
Depositing User : Symplectic Elements
Date Deposited : 23 Jun 2015 11:57
Last Modified : 23 Jun 2015 11:57
URI: http://epubs.surrey.ac.uk/id/eprint/807841

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800