This paper investigates the performance of three black-box optimizers exploiting separability on the 24 large-scale BBOB functions, including the Hooke-Jeeves method, MTS-LS1, and BSrr. Although BSrr was not specially designed for large-scale optimization, the results show that BSrr has a state-of-the-art performance on the five separable large-scale BBOB functions. The results show that the asymmetry significantly influences the performance of MTS-LS1. The results also show that the Hooke-Jeeves method performs better than MTS-LS1 on unimodal separable BBOB functions.
Cite
@article{arxiv.2204.13284,
title = {Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set},
author = {Ryoji Tanabe},
journal= {arXiv preprint arXiv:2204.13284},
year = {2022}
}
Comments
This is an accepted version of a paper to the Workshop on Black-Box Optimization Benchmarking (BBOB 2022) published in the companion volume of GECCO2022