Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Quantum-Behaved Simple Brain Storm Optimization with Simplex Search

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13344))

Included in the following conference series:

  • 912Accesses

Abstract

The simple brain storm optimization (SimBSO) algorithm is an adjusted algorithm to simplify the process of clustering in brain storm optimization algorithm (BSO). However, SimBSO has not significantly improved the optimization performance of BSO except for its simple algorithm structure. In this paper, a new algorithm named quantum-behaved simple brain storm optimization with simplex search (QSimplex-SimBSO) is proposed to improve the performance of SimBSO. In QSimplex-SimBSO, the quantum behavior is added into SimBSO to strengthen global searching capability and then the Nelder-Mead Simplex (NMS) method is used to enhance local searching capability. After large number of experiments on the Hedar set, the results show that QSimplex-SimBSO gets a better balance of global exploration and local exploitation by the visualizing confidence interval method. Meanwhile, QSimplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions.

This work was supported by the Basic and Applied Basic Research Funding Program of Guangdong Province (Grant No. 2019A1515111097), Yunnan Provincial Research Foundation for Basic Research, China (Grant No. 202001AU070041) and Guangdong Universities’ Special Projects in Key Fields of Natural Science (No.2019KZDZX1005).

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011).https://doi.org/10.1007/978-3-642-21515-5_36

    Chapter  Google Scholar 

  2. Jiang, Y., Chen, X., Zheng, F., Niyato, D., You, X.: Brain storm optimization-based edge caching in fog radio access networks. IEEE Trans. Veh. Technol.70(2), 1807–1820 (2021)

    Article  Google Scholar 

  3. Duan, H., Li, S., Shi, Y.: Predator-prey brain storm optimization for DC brushless motor. IEEE Trans. Magn.49(10), 5336–5340 (2013)

    Article  Google Scholar 

  4. Ma, X., Jin, Y., Dong, Q.: A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Appl. Soft Comput.54, 296–312 (2017)

    Article  Google Scholar 

  5. Zhu, H., Shi, Y.: Brain storm optimization algorithms with k-medians clustering algorithms. In: 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), pp. 107–110. IEEE, Chiang Mai (2015)

    Google Scholar 

  6. Zhan, Z., Zhang, J., Shi, Y., Liu, H.: A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE, Brisbane (2012)

    Google Scholar 

  7. Song, Z., Peng, J., Li, C., Liu, P.X.: A simple brain storm optimization algorithm with a periodic quantum learning strategy. IEEE Access6(19), 19968–19983 (2017)

    Google Scholar 

  8. Shi, Y.: Brain storm optimization algorithm in objective space. In: Congress on Evolutionary Computation (CEC). IEEE, Sendai (2015)

    Google Scholar 

  9. Cao, Y., et al.: A simple brain storm optimization algorithm via visualizing confidence intervals. Simul. Evol. Learn. 27–38 (2017)

    Google Scholar 

  10. Greiner, W.: Quantum mechanics. An introduction. 4th edn. J. Phys. Am. (2001)

    Google Scholar 

  11. Duan, H., Cong, L.: Quantum-behaved brain storm optimization approach to solving Loney’s solenoid problem. IEEE Trans. Magn.51(1Pt.2), 7000,307-1–7000,307-7 (2015)

    Google Scholar 

  12. Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 61–66. IEEE, Nagoya (1996)

    Google Scholar 

  13. Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 325–331. IEEE, Portland (2004)

    Google Scholar 

  14. Zhu, K., Jiang, M., Cheng, Y.: Niche artificial fish swarm algorithm based on quantum theory. In: IEEE 10th International Conference on Signal Processing Proceedings, pp. 1425–1428. IEEE, Beijing (2010)

    Google Scholar 

  15. Niu, Q., Zhou, T., Shiwei, M.: A quantum-inspired immune algorithm for hybrid flow shop with makespan criterion. J. Univ. Comput. Sci.15, 765–785 (2009)

    MathSciNet  Google Scholar 

  16. Wang, L., Niu, Q., Fei, M.: A novel quantum ant colony optimization algorithm. Bio-Inspired Computat. Intell. Appl.4688, 277–286 (2007)

    Google Scholar 

  17. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J.7, 308–313(1965)

    Google Scholar 

  18. Fan, S.-K.S., Zahara, E.: A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur. J. Oper. Res.181(2), 527–548 (2007)

    Article MathSciNet  Google Scholar 

  19. Chen, W., Cao, Y., Cheng, S., Sun, Y., Liu, Q., Li, Y.: Simplex search-based brain storm optimization. IEEE Access6(75), 75997–76006 (2018)

    Article  Google Scholar 

  20. Chelouah, R., Siarry, P.: Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur. J. Oper. Res.148(2), 335–348 (2003)

    Article MathSciNet  Google Scholar 

  21. Lin, H.: Hybridizing differential evolution and Nelder-Mead simplex algorithm for global optimization. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp. 198–202. IEEE, Wuxi (2016)

    Google Scholar 

  22. Chelouah, R., Siarry, P.: A hybrid method combining continuous Tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions. Eur. J. Oper. Res.161(3), 636–654 (2005)

    Article MathSciNet  Google Scholar 

  23. Dasril, Y. B., Wen, G.K.: Modified artificial bees colony algorithm with Nelder-Mead search algorithm. In: 2016 12th International Conference on Mathematics, Statistics, and Their Applications, pp.25–30. IEEE, Hatyai, Songkhla (2017)

    Google Scholar 

  24. Liu, Q., et al.: Benchmarking stochastic algorithms for global optimization problems by visualizing confidence intervals. IEEE Trans. Cybern.47(9), 1–14 (2017)

    Article MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dongguan University of Technology, University Road, Dongguan, 523808, China

    Xi Wang, Wei Chen, Qunfeng Liu & Yingying Cao

  2. Shaanxi Normal University, Chang An Avenue, Xi’an, 710119, China

    Shi Cheng

  3. Kunming University of Science and Technology, Jingming South Road, Kunming, 650500, China

    Yanmin Yang

Authors
  1. Xi Wang

    You can also search for this author inPubMed Google Scholar

  2. Wei Chen

    You can also search for this author inPubMed Google Scholar

  3. Qunfeng Liu

    You can also search for this author inPubMed Google Scholar

  4. Yingying Cao

    You can also search for this author inPubMed Google Scholar

  5. Shi Cheng

    You can also search for this author inPubMed Google Scholar

  6. Yanmin Yang

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toWei Chen.

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, X., Chen, W., Liu, Q., Cao, Y., Cheng, S., Yang, Y. (2022). Quantum-Behaved Simple Brain Storm Optimization with Simplex Search. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_34

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


[8]ページ先頭

©2009-2025 Movatter.jp