A scalable test suite for dynamic multiobjective optimization
Dynamic multiobjective optimization (DMO) 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 the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, 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 important, 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 is 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 is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Jiang, S., Kaiser, M., Yang, S., Kollias, S. and Krasnogor, N. (2019) A scalable test suite for dynamic multiobjective optimisation. IEEE Transactions on Cybernetics, pp.1-13.
ISSN : 2168-2267
Research Group : Institute of Artificial Intelligence (IAI)
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes