Two Novel Instance Selection Methods Combining Algorithm Performance and Landscape Analysis: A Comparative Study in Continuous Optimization

A reliable benchmark library is essential for advancing research in global optimization by enabling fair comparisons and rigorous testing of optimization algorithms across diverse problem landscapes. In this article, we focus on instance selection methods, which aim to choose representative problems...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. PP; S. 1 - 14
Hauptverfasser: Stripinis, Linas, Kudela, Jakub, Paulavicius, Remigijus
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 03.11.2025
Schlagworte:
Algorithm performance
Benchmark testing
black-box global optimization
Cybernetics
Euclidean distance
exploratory landscape analysis
instance selection methods
Libraries
numerical benchmarking
Optimization
Reliability
Reviews
Runtime
Training
Vectors
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:A reliable benchmark library is essential for advancing research in global optimization by enabling fair comparisons and rigorous testing of optimization algorithms across diverse problem landscapes. In this article, we focus on instance selection methods, which aim to choose representative problems for evaluating algorithm performance. We present a comprehensive review of existing instance selection methods, highlighting their strengths and limitations, particularly in balancing the consideration of algorithm performance and the analysis of problem characteristics using exploratory landscape analysis. Building on these insights, we introduce two novel instance selection methods that leverage both algorithm performance data and landscape analysis information to construct diverse and informative benchmark sets. For evaluation, we benchmark our approaches against four existing instance selection methods on the recently expanded DIRECTGOLib v2.0 library. Our results demonstrate that the proposed methods effectively identify representative instances that capture a wide range of problem characteristics, enabling a more comprehensive evaluation of algorithm performance. These findings have significant implications for the development and assessment of new optimization algorithms, ultimately contributing to more reliable and robust solutions for real-world optimization problems.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2025.3625095