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Frontiers of Computer Science

, Volume 12, Issue 2, pp 316–330 | Cite as

Sequential quadratic programming enhanced backtracking search algorithm

  • Wenting Zhao
  • Lijin Wang
  • Yilong Yin
  • Bingqing Wang
  • Yuchun Tang
Research Article

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.

Keywords

numerical optimization backtracking search algorithm sequential quadratic programming local search 

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Notes

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).

Supplementary material

11704_2016_5556_MOESM1_ESM.ppt (200 kb)
Supplementary material, approximately 200 KB.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Wenting Zhao
    • 1
  • Lijin Wang
    • 1
    • 2
  • Yilong Yin
    • 1
  • Bingqing Wang
    • 1
  • Yuchun Tang
    • 3
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.College of Computer and Information ScienceFujian Agriculture and Forestry UniversityFuzhouChina
  3. 3.Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanChina

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