Skip to main content
SpringerLink
Log in

Sequential quadratic programming enhanced backtracking search algorithm

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Holland J H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, Massachusettes: The MIT press, 1992

    Google Scholar 

  2. Storn R, Price K. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. Berkeley, CA: International Computer Science Institue, 1995

    MATH  Google Scholar 

  3. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, 26(1): 29–41

    Article  Google Scholar 

  4. Kennedy J, Eberhart R C. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. 1995, 1942–1948

    Chapter  Google Scholar 

  5. Eberhart R C, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science. 1995, 39–43

    Chapter  Google Scholar 

  6. Chen W N, Zhang J, Lin Y, Chen N, Zhan Z H, Chung H S H, Li Y, Shi Y H. Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 2013, 17(2): 241–258

    Article  Google Scholar 

  7. Yu W J, Shen M, Chen W N, Zhan Z H, Gong Y J, Lin Y, Liu O, Zhang J. Differential evolution with two-level parameter adaptation. IEEE Transactions on Cybernetics, 2014, 44(7): 1080–1099

    Article  Google Scholar 

  8. Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 2013, 219(15): 8121–8144

    Article  MathSciNet  MATH  Google Scholar 

  9. Agarwal S K, Shah S, Kumar R. Classification of mental tasks from eeg data using backtracking search optimization based neural classifier. Neurocomputing, 2015, 166: 397–403

    Article  Google Scholar 

  10. Yang D D, Ma H G, Xu D H, Zhang B H. Fault measurement for siso system using the chaotic excitation. Journal of the Franklin Institute, 2015, 352(8): 3267–3284

    Article  MathSciNet  Google Scholar 

  11. Zhang C J, Lin Q, Gao L, Li X Y. Backtracking search algorithm with three constraint handling methods for constrained optimization problems. Expert Systems with Applications, 2015, 42(21): 7831–7845

    Article  Google Scholar 

  12. Zhao W T, Wang L J, Yin Y L, Wang B Q, Wei Y, Yin Y S. An improved backtracking search algorithm for constrained optimization problems. In: Proceedings of the 7th International Conference on Knowledge Science, Engineering and Management. 2014, 222–233

    Google Scholar 

  13. Mallick S, Kar R, Mandal D, Ghoshal S. CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution. Journal of Experimental & Theoretical Artificial Intelligence, 2016, 28(4): 719–749

    Article  Google Scholar 

  14. Wang L T, Zhong Y W, Yin Y L, Zhao W T, Wang B Q, Xu Y L. A hybrid backtracking search optimization algorithm with differential evolution. Mathematical Problems in Engineering, 2015

    Google Scholar 

  15. Ali A F. A memetic backtracking search optimization algorithm for economic dispatch problem. Egyptian Computer Science Journal, 2015, 39(2)

    Google Scholar 

  16. Qian C, Yu Y, Zhou Z H. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2935–2941

    Google Scholar 

  17. Attaviriyanupap P, Kita H, Tanaka E, Hasegawa J. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Transactions on Power Systems, 2002, 17(2): 411–416

    Article  Google Scholar 

  18. Cai J J, Li Q, Li L X, Peng H P, Yang Y X. A hybrid CPSO–SQP method for economic dispatch considering the valve-point effects. Energy Conversion and Management, 2012, 53(1): 175–181

    Article  Google Scholar 

  19. Basu M. Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch. International Journal of Electrical Power & Energy Systems, 2013, 44(1): 591–596

    Article  Google Scholar 

  20. Morshed M J, Asgharpour A. Hybrid imperialist competitivesequential quadratic programming (HIC-SQP) algorithm for solving economic load dispatch with incorporating stochastic wind power: a comparative study on heuristic optimization techniques. Energy Conversion and Management, 2014, 84: 30–40

    Article  Google Scholar 

  21. Zhan Z H, Zhang J, Li Y, Shi Y H. Orthogonal learning particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2011, 15(6): 832–847

    Article  Google Scholar 

  22. Blum C, Puchinger J, Raidl G R, Roli A. Hybrid metaheuristics in combinatorial optimization: a survey. Applied Soft Computing, 2011, 11(6): 4135–4151

    Article  MATH  Google Scholar 

  23. Lozano M, García-Martínez C. Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report. Computers & Operations Research, 2010, 37(3): 481–497

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhang J, Zhan Z H, Lin Y, Chen N, Gong Y J, Zhong J H, Chung H, Li Y, Shi Y H. Evolutionary computation meets machine learning: a survey. Computational Intelligence Magazine, IEEE, 2011, 6(4): 68–75

    Article  Google Scholar 

  25. Nocedal J, Wright S. Sequential quadratic programming. In: Sun W Y, Yuan Y X, eds. Optimization Theory and Methods. Springer Optimization and Its Application, Vol 1. Springer Science & Business Media, 2006, 529–533

    Google Scholar 

  26. Wilson R B. A simplicial algorithm for concave programming. Dissertation for the Doctoral Degree. Cambridge, MA: Harvard University, 1963

    Google Scholar 

  27. Liang J J, Qu B Y, Suganthan P N, Hernández-Díaz A G. Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical Report. 2013

    Google Scholar 

  28. Qian H, Hu Y Q, Yu Y. Derivative-free optimization of highdimensional non-convex functions by sequential random embeddings. In: Preceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 1946–1952

    Google Scholar 

  29. Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report. 2005

    Google Scholar 

  30. Clerk M. Standard particle swarm optimisation. Technical Report. 2012

    Google Scholar 

  31. Hansen N, Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 2001, 9(2): 159–195

    Article  Google Scholar 

  32. Igel C, Hansen N, Roth S. Covariance matrix adaptation for multiobjective optimization. Evolutionary Computation, 2007, 15(1): 1–28

    Article  Google Scholar 

  33. Liang J J, Qin A K, Suganthan P N, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281–295

    Article  Google Scholar 

  34. Qin A K, Suganthan P N. Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of IEEE Congress on Evolutionary Computation. 2005, 1785–1791

    Google Scholar 

  35. Brest J, Greiner S, Boškovi´c B, Mernik M, Zumer V. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 646–657

    Article  Google Scholar 

  36. Suganthan P N, Hansen N, Liang J J, Deb K, Chen Y P, Auger A, Tiwari S. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL Report. 2005

    Google Scholar 

  37. Gong W Y, Cai Z H. Differential evolution with ranking-based mutation operators. IEEE Transactions on Cybernetics, 2013, 43(6): 2066–2081

    Article  Google Scholar 

  38. Loshchilov I. CMA-ES with restarts for solving CEC 2013 benchmark problems. In: Proceedings of IEEE Congress on Evolutionary Computation. 2013, 369–376

    Google Scholar 

  39. Zambrano-Bigiarini M, Clerc M, Rojas R. Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: Proceedings of IEEE Congress on Evolutionary Computation. 2013, 2337–2344

    Google Scholar 

  40. El-Abd M. Testing a particle swarm optimization and artificial bee colony hybrid algorithm on the CEC13 benchmarks. In: Proceedings of IEEE Congress on Evolutionary Computation. 2013, 2215–2220

    Google Scholar 

  41. Dos Santos Coelho L, Ayala H V H. Population’s variance-based adaptive differential evolution for real parameter optimization. In: Proceedings of IEEE Congress on Evolutionary Computation. 2013, 1672–1677

    Google Scholar 

  42. Nepomuceno F V, Engelbrecht A P. A self-adaptive heterogeneous PSO for real-parameter optimization. In: Proceedings of IEEE Congress on Evolutionary Computation. 2013, 361–368

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yilong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bingqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuchun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation