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Improved NSGA-III using neighborhood information and scalarization

Khan, Burhan, Johnstone, Michael, Hanoun, Samer, Lim, Chee Peng, Creighton, Douglas and Nahavandi, Saeid 2017, Improved NSGA-III using neighborhood information and scalarization, in SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 3033-63038.

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Title Improved NSGA-III using neighborhood information and scalarization
Author(s) Khan, Burhan
Johnstone, MichaelORCID iD for Johnstone, Michael orcid.org/0000-0002-3005-8911
Hanoun, Samer
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Creighton, Douglas
Nahavandi, Saeid
Conference name Systems, Man, and Cybernetics. International Conference (2016 : Budapest, Hungary)
Conference location Budapest, Hungary
Conference dates 9-12 Oct. 2016
Title of proceedings SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2017
Conference series Systems, Man, and Cybernetics International Conference
Start page 3033
End page 63038
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) many-objective optimization
pareto-dominance
scalarization
penalty-based boundary intersection
evolutionary computation
Summary Recent efforts in the evolutionary multi-objectiveoptimization (EMO) community focus on addressingshortcomings of current solution techniques adopted for solvingmany-objective optimization problems (MaOPs). One suchchallenge faced by classical multi-objective evolutionaryalgorithms is diversity preservation in optimization problems withmore than three objectives, namely MaOPs. In this vein, NSGAIIIhas replaced the crowding distance measure in NSGA-II withreference points in the objective space to ensure diversity of theconverged solutions along the pre-determined solutions in theenvironmental selection phase. NSGA-III uses the Paretodominanceprinciple to obtain the non-dominated solutions in theenvironmental selection phase. However, the Pareto-dominanceprinciple loses its selection pressure in high-dimensionaloptimization problems, because most of the obtained solutionsbecome non-dominated. Inspired by θ-DEA, we address theselection pressure issue in NSGA-III, by exploiting thedecomposition principle of MOEA/D using reference points formultiple single-objective optimization problems. Moreover,similar to MOEA/D, the parent selection process is restricted tothe neighboring solutions, as opposed to random selection ofparent solutions from the entire population in NSGA-III. Theeffectiveness of the proposed method is demonstrated on differentwell-known benchmark optimization problems for 3- to 10-objectives. The results compare favorably with those fromMOEA/D, NSGA-III, and θ-DEA.
ISBN 9781509018970
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category EN Other conference paper
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30091966

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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