intLyc/MToP-MTOData

MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking (Pre-run Experimental Data)

This repository hosts the extensive pre-run experimental data generated by the MToP (Multitask Optimization Platform), as presented in the paper MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking.

MToP is the first open-source benchmarking platform for Evolutionary Multitasking (EMT), designed to concurrently address multiple optimization tasks within limited computing resources. This dataset provides comprehensive experimental results from the MToP platform, aiming to enhance reproducibility and reduce computational overhead for researchers working with Multitask Evolutionary Algorithms (MTEAs).

The MToP platform incorporates:

• Over 50 Multitask Evolutionary Algorithms (MTEAs) for multitask optimization.
• Over 50 adapted single-task evolutionary algorithms for multitask optimization problems.
• More than 200 Multitask Optimization (MTO) problem cases, including real-world applications.
• Over 150 classical single-task optimization benchmark problems.
• Over 20 performance metrics covering single- and multi-objective optimization.

This dataset comprises experimental results of various algorithms on benchmark suites over 30 independent runs under fixed random seeds, facilitating comparative analyses and research in evolutionary multitasking.

Links

• Paper: MToP: A MATLAB Benchmarking Platform for Evolutionary Multitasking
• MToP Platform Code (GitHub): https://github.com/intLyc/MTO-Platform
• Project Page: MTO Website

Data Download

You can download the data from this repository directly using git lfs:

git lfs install
git clone https://huggingface.co/datasets/intLyc/MToP-MTOData

Citation

If you find this platform or dataset useful for your research, please cite the original paper:

@Article{Li2023MToP,
  title   = {{MToP}: A {MATLAB} Benchmarking Platform for Evolutionary Multitasking},
  author  = {Yanchi Li and Wenyin Gong and Tingyu Zhang and Fei Ming and Shuijia Li and Qiong Gu and Yew-Soon Ong},
  journal = {arXiv preprint arXiv:2312.08134},
  year    = {2023},
  eprint  = {2312.08134},
}