论文标题
大规模BBOB函数集的基准测试Hooke-Jeeves方法，MTS-LS1和BSRR
Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set
论文作者
论文摘要
本文研究了三个黑盒优化器的性能，这些黑盒优化器在24个大规模BBOB函数上利用可分离性，包括Hooke-Jeeves方法，MTS-LS1和BSRR。尽管BSRR不是专门为大规模优化设计的，但结果表明，BSRR在五个可分开的大规模BBOB函数上具有最先进的性能。结果表明，不对称会显着影响MTS-LS1的性能。结果还表明，在单峰可分开的BBOB函数上，胡克 - 吉维斯方法的性能优于MTS-LS1。
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.
