Decomposition based dominance relationship for evolutionary many-objective algorithm

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings
PublisherIEEE
ISBN (Electronic)9781538627266, 9781538627259
ISBN (Print)9781538627273, 9781538640586
Publication statusPublished - Dec 2017

Conference

Title2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017)
LocationHilton Hawaiian Village Waikiki Resort
PlaceUnited States
CityHonolulu, Hawaii
Period27 November - 1 December 2017

Abstract

Decomposition based evolutionary algorithms have achieved great success in solving many-objective optimization problems. However, the design of proper decomposition vectors is not an easy task, especially in high dimensional objective space. In this paper, we study how to better use these decomposition vectors. We first show that for any given decomposition vector, new dominance relationship and crowding measurement strategy can be well defined. Based on them, we then propose a new evolutionary algorithm for many objective optimization. By this way, the utilization efficiency of decomposition vectors is enhanced and thus the task of weights design is alleviated accordingly. Experiments are conducted to compare the proposed algorithm with four state-of-the-art decomposition based evolutionary algorithms on a set of well-known many-objective test problems with 5 to 10 objectives. The simulation results show that the proposed algorithm can achieve comparable results with fewer decomposition vectors.

Research Area(s)

  • decomposition, dominance relationship, evolutionary algorithm, Many-objective

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Decomposition based dominance relationship for evolutionary many-objective algorithm. / Chen, Lei; Liu, Hai-Lin; Tan, Kay Chen.

2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. IEEE, 2017.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review