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Sequential quadratic programming enhanced backtracking search algorithm

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Abstract

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm (BSA) and sequential quadratic programming (SQP). BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

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Acknowledgements

This work was supported by the NSFC-Guangdong Joint Fund (U1201258), the National Natural Science Foundation of China (Grant No. 61573219), the Shandong Natural Science Funds for Distinguished Young Scholars (JQ201316), the Fundamental Research Funds of Shandong University (2014JC028), and the Natural Science Foundation of Fujian Province of China (2016J01280).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Wenting Zhao, Lijin Wang, Yilong Yin & Bingqing Wang

  2. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Lijin Wang

  3. Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, 250012, China

    Yuchun Tang

Authors
  1. Wenting Zhao
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  2. Lijin Wang
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  3. Yilong Yin
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  4. Bingqing Wang
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  5. Yuchun Tang
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Corresponding author

Correspondence to Yilong Yin.

Additional information

Wenting Zhao received her BS degree in electrical engineering from Shandong University (SDU), China in 2014. Currently, she is studying at SDU for a master degree in software engineering. Her main research interests are evolutionary computation and machine learning.

Lijin Wang received his BS and MS degrees from Fujian Agriculture and Forestry University (FAFU), China in 2000 and 2005 respectively, and his PhD degree from Beijing Forestry University, China in 2008. He is currently a post-doctoral fellow with the School of Computer Science and Technology, Shandong University, China. He is also an associate professor with the College of Computer and Information Science, FAFU. His research interests include evolutionary algorithms and intelligent information processing.

Yilong Yin received his PhD degree from Jilin University, China in 2000. From 2000 to 2002, he worked as a postdoctoral fellow in the Department of Electronics Science and Engineering, Nanjing University, China. He is currently the director of MLA Group and a professor of the School of Computer Science and Technology, Shandong University, China. His research interests include machine learning, data mining, and computational medicine.

Bingqing Wang received his BS degree in electrical engineering from Qingdao University, China in 2012. From 2012 to 2016, he received his master degree in School of Computer Science and Technology, Shandong University, China. His main research interests are machine learning and application.

Yuchun Tang received his MD degree majored in sectional and imaging anatomy at Shandong University, China in 2009. He is currently a teacher in Shandong University School of Medicine, China. His research interests include sectional and imaging anatomy, brain imaging, and computational neuroscience.

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Zhao, W., Wang, L., Yin, Y. et al. Sequential quadratic programming enhanced backtracking search algorithm. Front. Comput. Sci. 12, 316–330 (2018). https://doi.org/10.1007/s11704-016-5556-9

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  • DOI: https://doi.org/10.1007/s11704-016-5556-9

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