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Corner sort for pareto-based many-objective optimization

Wang, H and Yao, X (2014) Corner sort for pareto-based many-objective optimization IEEE Transactions on Cybernetics, 44 (1). pp. 92-102.

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Abstract

Nondominated sorting plays an important role in Pareto-based multiobjective evolutionary algorithms (MOEAs). When faced with many-objective optimization problems multiobjective optimization problems (MOPs) with more than three objectives, the number of comparisons needed in nondominated sorting becomes very large. In view of this, a new corner sort is proposed in this paper. Corner sort first adopts a fast and simple method to obtain a nondominated solution from the corner solutions, and then uses the nondominated solution to ignore the solutions dominated by it to save comparisons. Obtaining the nondominated solutions requires much fewer objective comparisons in corner sort. In order to evaluate its performance, several state-of-the-art nondominated sorts are compared with our corner sort on three kinds of artificial solution sets of MOPs and the solution sets generated from MOEAs on benchmark problems. On one hand, the experiments on artificial solution sets show the performance on the solution sets with different distributions. On the other hand, the experiments on the solution sets generated from MOEAs show the influence that different sorts bring to MOEAs. The results show that corner sort performs well, especially on many-objective optimization problems. Corner sort uses fewer comparisons than others. © 2013 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Wang, Hhanding.wang@surrey.ac.ukUNSPECIFIED
Yao, XUNSPECIFIEDUNSPECIFIED
Date : 1 January 2014
Identification Number : 10.1109/TCYB.2013.2247594
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:39
Last Modified : 17 May 2017 15:12
URI: http://epubs.surrey.ac.uk/id/eprint/839968

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