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An improved NSGA-III algorithm based on elimination operator for many-objective optimization

Content Provider	Springer Nature Link
Author	Bi, Xiaojun Wang, Chao
Copyright Year	2017
Abstract	Due to the low selection pressure of the Pareto-dominance relation and the ineffectivity of diversity maintenance schemes in the environmental selection, the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs) fail to handle many-objective optimization problems. The recently presented non-dominated sorting genetic algorithm III (NSGA-III) employs the uniformly distributed reference points to significantly promote population diversity, but the convergence based on the Pareto-dominance relation could still be enhanced. For this purpose, an improved NSGA-III algorithm based on elimination operator (NSGA-III-EO) is proposed. In the proposed algorithm, the elimination operator first identifies the reference point with maximum niche count and then employs the penalty-based boundary intersection distance to rank the individuals associated with it. To this end, the selection scheme is used to remove the worse individuals rather than to select the superior individuals. The proposed NSGA-III-EO is tested on a number of well-known benchmark problems with up to fifteen objectives and shows the competitive performance compared with five state-of-the-art MOEAs. Additionally, it is also tested on constrained problems having a large number of objectives and shows good performance.
Starting Page	361
Ending Page	383
Page Count	23
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	9
Issue Number	4
e-ISSN	18659292
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2017-07-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Many-objective optimization NSGA-III Convergence Elimination operator Constrained optimization Mathematical and Computational Engineering Artificial Intelligence (incl. Robotics) Complex Systems Control, Robotics, Mechatronics Bioinformatics Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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