A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

Shouyong Jiang, Marcus Kaiser, Shengxiang Yang, Stefanos Kollias, Natalio Krasnogor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2814-2826
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number6
Early online date15 Feb 2019
Publication statusPublished - Jun 2020


  • Adversarial examples
  • dynamic multiobjective optimization (DMO)
  • dynamics
  • Pareto front
  • scalable test problems


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