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Lookup NU author(s): Dr Shouyong Jiang,
Professor Marcus Kaiser,
Professor Natalio Krasnogor
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2020.
For re-use rights please refer to the publisher's terms and conditions.
Dynamic multiobjective optimisation has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of existing dynamic multiobjective test problems have not been rigorously constructed and analysed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more importantly, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features are then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite are more challenging to the dynamic multiobjective optimisation algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites can not.
Author(s): Jiang S, Kaiser M, Yang S, Kollias S, Krasnogor N
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Cybernetics
Print publication date: 01/06/2020
Online publication date: 15/02/2019
Acceptance date: 16/01/2019
Date deposited: 29/01/2019
ISSN (print): 2168-2267
ISSN (electronic): 2168-2275
Publisher: Institute of Electrical and Electronics Engineers
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