A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation
This work addresses the need for better benchmarking tools in dynamic multiobjective optimization, which is incremental as it builds on prior test suites by adding new features and scalability.
The authors tackled the lack of rigorously constructed test problems in dynamic multiobjective optimization by identifying biases in existing suites and developing a new scalable continuous test suite that includes diverse, real-life dynamics, which was shown to be more challenging and capable of testing algorithms in ways existing suites cannot.
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.