Selection methods and diversity preservation in many-objective evolutionary algorithms

Martí, Luis; Segredo, Eduardo; Sánchez-Pi, Nayat; Hart, Emma

Abstract

Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. Design/methodology/approach – Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group (WFG) test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings – The computational results highlight that the 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 crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value – The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of our knowledge, it has not been investigated yet.

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2018
• Online Publication Date: Aug 23, 2018
• Publication Date: Sep 4, 2018
• Deposit Date: Jun 25, 2018
• Publicly Available Date: Aug 23, 2018
• Journal: Data Technologies and Applications
• Print ISSN: 2514-9288
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1108/dta-01-2018-0009
• Keywords: multi-objective evolutionary algorithm; many-objective optimisation; selection mechanism; diversity preservation; convergence; Walking Fish Group (WFG) test suite
• Public URL: http://researchrepository.napier.ac.uk/Output/1234681
• Contract Date: Jun 25, 2018