Evolutionary many-objective optimization by MO-NSGA-II with enhanced mating selection

Shao Wen Chen, Tsung-Che Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Many-objective optimization deals with problems with more than three objectives. The rapid growth of non-dominated solutions with the increase of the number of objectives weakens the search ability of Pareto-dominance-based multiobjective evolutionary algorithms. MO-NSGA-II strengthens its dominance-based predecessor, NSGA-II, by guiding the search process with reference points. In this paper, we further improve MO-NSGA-II by enhancing its mating selection mechanism with a hierarchical selection and a neighborhood concept based on the reference points. Experimental results confirm that the proposed ideas lead to better solution quality.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1397-1404
Number of pages8
ISBN (Electronic)9781479914883
DOIs
Publication statusPublished - 2014 Sep 16
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Other

Other2014 IEEE Congress on Evolutionary Computation, CEC 2014
CountryChina
CityBeijing
Period14/7/614/7/11

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Chen, S. W., & Chiang, T-C. (2014). Evolutionary many-objective optimization by MO-NSGA-II with enhanced mating selection. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1397-1404). [6900400] (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900400