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Generating continuous multi-objective benchmark problems by the object-oriented method
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AI
DOI：10.1016/j.swevo.2025.102242.png)
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Abstract
En 中文
Designing multi-objective benchmark test functions is an important topic in the field of evolutionary multi-objective optimization because it can help researchers identify the strengths and weaknesses of algorithms and contribute to improving their performance. However, existing methods for constructing multi-objective test problems exhibit certain specific limitations, such as the homogeneous structure of objectives, regular shapes of the Pareto optimal sets, and so on. To address these issues, this paper proposes an object-oriented construction method that abstracts components of a multi-objective optimization problem’s fitness landscape into classes. By designing attributes of these components, such as size, shape, position, and quantity, diverse test classes can be generated. Through a combination of these attributes, test cases with varying difficulty levels and distinct characteristics can be constructed. Based on this generator, several novel features are displayed. Moreover, five classic multi-objective evolutionary algorithms are tested on them. The results show different behaviors of these algorithms on the constructed test cases. At the same time, these generated features pose significant challenges to the tested algorithms.
Keywords:
Multi-objective optimization
Benchmark test functions
Problem features
Object-oriented design
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