Skip to main content

Research Repository

Advanced Search

Impact of selection methods on the diversity of many-objective Pareto set approximations

Mart�, Luis; Segredo, Eduardo; S�nchez-Pi, Nayat; Hart, Emma


Luis Mart�

Eduardo Segredo

Nayat S�nchez-Pi


Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator-based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. Algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hyper-volume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where the the crowding distance is replaced by the Euclidean distance, yield the best results.

Presentation Conference Type Conference Paper (published)
Conference Name 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Start Date Sep 6, 2017
End Date Sep 8, 2017
Acceptance Date Apr 22, 2017
Online Publication Date Sep 1, 2017
Publication Date Sep 1, 2017
Deposit Date Apr 24, 2017
Publicly Available Date Apr 25, 2017
Journal Procedia Computer Science
Print ISSN 1877-0509
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 112
Pages 844-853
Series ISSN 1877-0509
Keywords multi-objective evolutionary algorithm; many-objective optimization; selection mechanism; diversity preservation
Public URL
Additional Information Special Issue - Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France
Contract Date Apr 24, 2017


You might also like

Downloadable Citations