Competition on Dynamic Multimodal Optimization

This competition concentrates on DMMO problems in which the goal is finding and tracking multiple global minima. It intends to provide a fair, comprehensive, and accessible platform for benchmarking, evaluating, and comparing different DMMO methods for researchers in this field.

The codes of these problems are available in MATLAB and Python (verified with Python 3.7). A technical report is provided with detailed information on how to benchmark a method and report the results. Both codes were structured to minimize the effort required by the participants to simulate and optimize a problem. Results of an exemplary optimization method are provided for the participants. Both MATLAB and Python platforms have an exemplary optimization method that can optimize and report results for a given problem, dynamic scenario, and run number. For researchers experienced in static multimodal optimization, the code of a recent prediction method is also provided which can easily be integrated with any static multimodal optimization method to create a DMMO method.

A proposal for this competition has been submitted to CEC'2022. All the relevant documents, including the problem definitions and evaluation criteria, Python platform, MATLAB platform, and sample result files will be uploaded to this website after acceptance of the competition proposal.

To participate in the competition, please submit:

• your result file folder as a zip file (as in sample result files) AND

• a document including the name and affiliation of the participants and a short description of the used method.

by 1 July 2022 to Ali Ahrari (aliahrari1983@gmail.com)

Download Links:

Problem Definition

Code (Python)

Code (MATLAB)

Sample Result Files

Organizers