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Minimax optimal designs via particle swarm optimization methods

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Abstract

Particle swarm optimization (PSO) techniques are widely used in applied fields to solve challenging optimization problems but they do not seem to have made an impact in mainstream statistical applications hitherto. PSO methods are popular because they are easy to implement and use, and seem increasingly capable of solving complicated problems without requiring any assumption on the objective function to be optimized. We modify PSO techniques to find minimax optimal designs, which have been notoriously challenging to find to date even for linear models, and show that the PSO methods can readily generate a variety of minimax optimal designs in a novel and interesting way, including adapting the algorithm to generate standardized maximin optimal designs.

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Acknowledgments

Weng Kee Wong worked on the manuscript when he was a visiting fellow at The Sir Isaac Newton Institute at Cambridge, England and a scientific advisor for a 6-month workshop in the design and analysis of experiment. He would like to thank Professor Rosemary Bailey for hosting the workshop and the Institute for the support during his repeated visits there in the second half of 2011. This research of Weng Kee Wong was also partially supported by NSC, Mathematics Research Promotion Center with grant number 102-51 and the Ministry of Education, Taiwan, R.O.C. The Aim for the Top University Project to the National Cheng Kung University (NCKU). The research of Ray-Bing Chen was partially supported by the National Science Council of Taiwan with grant number 101-2118-M-006-002-MY2 and the Mathematics Division of the National Center for Theoretical Sciences (South) in Taiwan. The research of Weichung Wang was partially supported by the National Science Council of Taiwan and the Taida Institute of Mathematical Sciences. We thank the editorial team for all the helpful comments and suggestions.

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Authors and Affiliations

  1. Department of Statistics, National Cheng-Kung University, Tainan , 70101, Taiwan

    Ray-Bing Chen & Heng-Chih Tung

  2. Department of Digital Fashion Design, Toko University, Puzih, Chiayi , 61363, Taiwan

    Shin-Perng Chang

  3. Institute of Applied Mathematical Sciences, National Taiwan University, Taipei , 10617, Taiwan

    Weichung Wang

  4. Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA , 90095-1772, USA

    Weng Kee Wong

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  1. Ray-Bing Chen
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  2. Shin-Perng Chang
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  3. Weichung Wang
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  4. Heng-Chih Tung
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  5. Weng Kee Wong
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Correspondence to Weichung Wang.

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Chen, RB., Chang, SP., Wang, W. et al. Minimax optimal designs via particle swarm optimization methods. Stat Comput 25, 975–988 (2015). https://doi.org/10.1007/s11222-014-9466-0

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