Many real-world optimization problems appear to not only have multiple objectives that conflict each other but also change over time. They are dynamic multi-objective optimization problems (DMOPs) and the corresponding field is called dynamic multi-objective optimization (DMO), which has gained growing attention in recent years. However, one main issue in the field of DMO is that there is no standard test suite to determine whether an algorithm is capable of solving them. This paper presents a new benchmark generator for DMOPs that can generate several complicated characteristics, including mixed Pareto-optimal front (convexity-concavity), strong dependencies between variables, and a mixed type of change, which are rarely tested in the literature. Experiments are conducted to compare the performance of five state-of-the-art DMO algorithms on several typical test functions derived from the proposed generator, which gives a better understanding of the strengths and weaknesses of these tested algorithms for DMOPs.
|Title of host publication||2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings|
|Editors||Daniel Neagu, Mariam Kiran, Paul Trundle|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 17 Oct 2014|
|Event||2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Bradford, West Yorkshire, United Kingdom|
Duration: 8 Sept 2014 → 10 Sept 2014
|Name||2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings|
|Conference||2014 14th UK Workshop on Computational Intelligence, UKCI 2014|
|City||Bradford, West Yorkshire|
|Period||8/09/14 → 10/09/14|
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© 2014 IEEE.