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Modified DIRECT Algorithm for Scaled Global Optimization Problems

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Advances in Global Optimization

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 95))

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Abstract

DIRECT is a popular deterministic algorithm for global optimization problems. It can find the basins of attraction for global or local optima efficiently, especially when dimension is small. Recently, we have proposed a class of modified DIRECT algorithms to eliminate the sensitivities of the original DIRECT to linear scaling of the objective function. In this paper, we devote to find a specific algorithm with best performance among this class. We compare the performance of the modified DIRECT algorithms on the GKLS test set. Numerical results show that DIRECT-median performs outstanding among this class. What is more, numerical results also show that DIRECT-median can find solutions with high accuracy much more efficiently than the original DIRECT.

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Acknowledgements

This work was supported by NSF of China (No.11271069) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.13YJC630095).

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Authors and Affiliations

  1. Dongguan University of Technology, Dongguan, China

    Qunfeng Liu, Jianxiong Zhang & Fen Chen

Authors
  1. Qunfeng Liu
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  2. Jianxiong Zhang
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  3. Fen Chen
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Corresponding author

Correspondence to Qunfeng Liu .

Editor information

Editors and Affiliations

  1. School of Science, Information Technology and Engineering, Federation University Australia, Ballarat, Victoria, Australia

    David Gao

  2. School Science, Inform., Tech. & Engg., Federation University Australia, Ballarat, Victoria, Australia

    Ning Ruan

  3. Dept. of Mathematical Sciences, Tsinghua University, Beijing, China

    Wenxun Xing

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Liu, Q., Zhang, J., Chen, F. (2015). Modified DIRECT Algorithm for Scaled Global Optimization Problems. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_40

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