A Scalable Test Suite for Continuous 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...

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Vydané v:IEEE transactions on cybernetics Ročník 50; číslo 6; s. 2814 - 2826
Hlavní autori: Jiang, Shouyong, Kaiser, Marcus, Yang, Shengxiang, Kollias, Stefanos, Krasnogor, Natalio
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adversarial examples
Algorithms
Benchmark testing
Computer science
Cybernetics
dynamic multiobjective optimization (DMO)
dynamics
Empirical analysis
Heuristic algorithms
Multiple objective analysis
Optimization
Pareto front
Scalability
scalable test problems
Shape
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí: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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2896021