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This competition aims to provide a fair platform for unbiased, comprehensive, and informative evaluation and comparison of methods for box-constrained continuous multimodal optimization. It employs a new set of fully scalable and tunable test problems that simulate diverse challenges associated with multimodal optimization. These test problems were designed to address some drawbacks of the well-known CEC’2013 test suite for benchmarking niching methods for multimodal optimization. The new test suite aims to not only differentiate relevant methods but also pinpoint their strengths and weaknesses more reliably. A user-friendly platform for the test problems will be provided in MATLAB and Python which can easily be integrated with almost any existing multimodal optimization method.
To participate in the competition, please read the Competition Description and submit:
your result file folder as a zip file (see Sample Result Files) AND
a document including the name and affiliation of the participants and a short description of the used method, especially the parameter setting (see the Competition Description)
by 1 July 2024 to Ali Ahrari (a.ahrari@unsw.edu.au)
Organizers
Jonathan Fieldsend
University of Exeter, UK
(J.E.Fieldsend@exeter.ac.uk)
Mike Preuss
Universiteit Leiden, Netherlands
(m.preuss@liacs.leidenuniv.nl)
Xiaodong Li
RMIT University, Australia
(xiaodong.li@rmit.edu.au)
Michael G. Epitropakis
Lancaster University, UK
(m.epitropakis@lancaster.ac.uk)
Competition Outcome
Result Summary (download)
Winner: TRDE-LR (Tabu Restart-Based Niching Differential Evolution Algorithm with Local Refinement)
Runner-up: RR-CMA-ES (CMA-ES with repelling restarts)
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