  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 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 dynamic
multiobjective optimisation algorithms found in the literature. The test suite
can also test algorithms in ways that existing test suites can not.
