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arXiv · 2204.13284

Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set

Ryoji Tanabe
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Abstract

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.

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Ryoji Tanabe. 2022-04-28. Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set. https://doi.org/10.1145/3520304.3533951

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