A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

Jiang, Shouyong and Kaiser, Marcus and Yang, Shengxiang and Kollias, Stefanos and Krasnogor, Natalio (2019) A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization. IEEE Transactions on Cybernetics . pp. 1-13. ISSN 2168-2267

Full content URL: https://doi.org/10.1109/TCYB.2019.2896021

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A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

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Abstract

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 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 are 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 are more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.

Keywords:adversarial examples, dynamic multiobjective optimization, dynamics, pareto front, scalable test problems
Subjects:G Mathematical and Computer Sciences > G510 Information Modelling
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:34953
Deposited On:25 Mar 2019 16:53

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