[DATA SUBMISSION] BSrr, HJ-5, HJ-9, MTSLS1-5, and MTSLS1-5 on the bbob-largescale suite

[ryojitanabe]

Dear Organizers of COCO,

I would like to submit the benchmarking results of five optimizers (BSrr, HJ-5, HJ-9, MTSLS1-5, and MTSLS1-5) on the bbob-largescale suite.

Best regards, Ryoji

Reference

Ryoji Tanabe: Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set, submitted to the BBOB2022 workshop.

Description of the Algorithm(s)

I implemented the Hooke-Jeeves method (HJ) and MTS-LS1 in C. I used a slightly modified version of the Python2 implementation of BSrr provided by the authors (https://github.com/pasky/step). I benchmarked HJ and MTS-LS1 with two step-size values (c=0.5 and c=0.9). The following list summarizes the five optimizers:

• BSrr is the BSrr with the default parameter setting.
• HJ-5 is the HJ with c=0.5.
• HJ-9 is the HJ with c=0.9.
• MTSLS1-5 is the MTS-LS1 with c=0.5.
• MTSLS1-9 is the MTS-LS1 with c=0.9.

Link to Data

https://drive.google.com/drive/folders/1VD548_H30AJjoof221OvMzt-JAu5CqYd?usp=sharing

Optional: Source Code of Experiment

https://github.com/ryojitanabe/largebbob2022

[brockho]

Many thanks, @ryojitanabe, for your contributions to COCO. All five data sets are finally available via the postprocessing (after potentially running cocopp.archives.update_all() if you have used COCO before).