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Lookup NU author(s): Dr Shouyong Jiang, Professor Marcus Kaiser, Professor Natalio KrasnogorORCiD
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© 2018 IEEE. Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.
Author(s): Jiang S, Kaiser M, Wan S, Guo J, Yang S, Krasnogor N
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Year of Conference: 2018
Online publication date: 04/10/2018
Acceptance date: 08/07/2018
Publisher: IEEE
URL: https://doi.org/10.1109/CEC.2018.8477667
DOI: 10.1109/CEC.2018.8477667
Library holdings: Search Newcastle University Library for this item
ISBN: 9781509060177
