Towards improved benchmarking of black-box optimization algorithms using clustering problems

Gallagher, Marcus (2016) Towards improved benchmarking of black-box optimization algorithms using clustering problems. Soft Computing, 1-15. doi:10.1007/s00500-016-2094-1


Author Gallagher, Marcus
Title Towards improved benchmarking of black-box optimization algorithms using clustering problems
Journal name Soft Computing   Check publisher's open access policy
ISSN 1433-7479
1432-7643
Publication date 2016-03-10
Year available 2016
Sub-type Article (original research)
DOI 10.1007/s00500-016-2094-1
Open Access Status Not Open Access
Start page 1
End page 15
Total pages 15
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Abstract The field of Metaheuristics has produced a large number of algorithms for continuous, black-box optimization. In contrast, there are few standard benchmark problem sets, limiting our ability to gain insight into the empirical performance of these algorithms. Clustering problems have been used many times in the literature to evaluate optimization algorithms. However, much of this work has occurred independently on different problem instances and the various experimental methodologies used have produced results which are frequently incomparable and provide little knowledge regarding the difficulty of the problems used, or any platform for comparing and evaluating the performance of algorithms. This paper discusses sum of squares clustering problems from the optimization viewpoint. Properties of the fitness landscape are analysed and it is proposed that these problems are highly suitable for algorithm benchmarking. A set of 27 problem instances (from 4-D to 40-D), based on three well-known datasets, is specified. Baseline experimental results are presented for the Covariance Matrix Adaptation-Evolution Strategy and several other standard algorithms. A web-repository has also been created for this problem set to facilitate future use for algorithm evaluation and comparison.
Keyword Algorithm benchmarking
Clustering
Continuous black-box optimization
Fitness landscape analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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