Skip to main content
SpringerLink
Log in

A Mapping Solution Search Garden Balsam Optimization for Solving Invariant Point Problems

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2022)

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

Included in the following conference series:

  • 702 Accesses

Abstract

Mapping solution search (MSS), which maps current solution to a mapping solution, increases population diversity and promotes algorithm without the difficulty of premature convergence. This paper presents an MSS based garden balsam optimization (MGBO). This avoids the premature convergence of the algorithm, improves the convergence speed of the algorithm, and increases the possibility that the solution is closer to the global optimum. To evaluate the performance of MGBO, four complex invariant point problems are chosen from the literature. Experimental studies show that the MGBO can solve these problems with great precision compared with some state-of-the-art algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • 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

Institutional subscriptions

References

  1. Hirstoaga, S.A.: Iterative selection methods for common fixed point problems. J. Math. Anal. Appl. 324(2), 1020–1035 (2020)

    Article  MathSciNet  Google Scholar 

  2. Pakkaranang, N., Kumam, P., Cho, Y.J.: Proximal point algorithms for solving convex minimization problem and common fixed points problem of asymptotically quasi-nonexpansive mappings in cat(0) spaces with convergence analysis. Numer. Algorithms 78(3), 827–845 (2018)

    Article  MathSciNet  Google Scholar 

  3. Abed, S.S., Hasan, Z.: Convergence comparison of two schemes for common fixed points with an application. Ibn AL-Haitham J. Pure Appl. Sci. 32(2), 81(2019)

    Google Scholar 

  4. Chidume, C.E., Romanus, O.M., Nnyaba, U.V.: An iterative algorithm for solving split equality fixed point problems for a class of nonexpansive-type mappings in banach spaces. Numer. Algorithms (4), 1–21 (2019)

    Google Scholar 

  5. Ogbuisi, F.U., Mewomo, O.T.: On split generalised mixed equilibrium problems and fixed-point problems with no prior knowledge of operator norm. J. Fixed Point Theory Appl. 19(3), 2109–2128 (2017). https://doi.org/10.1007/s11784-016-0397-6

    Article  MathSciNet  MATH  Google Scholar 

  6. Spaces, B.R.H.: Weak and strong convergence theorems for the multiple-set split equality common fixed-point problems of demicontractive mappings. J. Func. Spaces 2017(2), 1–11 (2017)

    MathSciNet  Google Scholar 

  7. Berdahl, A., Torney, C.J., Ioannou, C.C., Faria, J.J., Couzin, I.D.: Emergent sensing of complex environments by mobile animal groups. Science 339(6119), 574–576 (2018)

    Article  Google Scholar 

  8. Li, S., Sun, Y.: A novel numerical optimization algorithm inspired from garden balsam. Neural Comput. Appl. 32(22), 16783–16794 (2018). https://doi.org/10.1007/s00521-018-3905-3

    Article  Google Scholar 

  9. Li, S., Sun, Y.: Garden balsam optimization algorithm. Concurr. Comput. Pract. Exper. 32(2), e5456 (2020)

    Google Scholar 

  10. Li, S., Sun, Y.: Predicting ink transfer rate of 3D additive printing using EGBO optimized least squares support vector machine model. Math. Probl. Eng. (2020)

    Google Scholar 

  11. Watson, G.A. (ed.): Numerical Analysis. LNM, vol. 773. Springer, Heidelberg (1980). https://doi.org/10.1007/BFb0094158

    Book  Google Scholar 

  12. Tizhoosh H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings international conference on computational intelligence for modelling control and automation, CIMCA2005, vol. 1, pp. 695–701. Vienna, Austria (2005)

    Google Scholar 

  13. Naidu, Y.R., Ojha, A.K.: A space transformational invasive weed optimization for solving fixed-point problems. Appl. Intell. 48(4), 942–952 (2017). https://doi.org/10.1007/s10489-017-1021-1

    Article  Google Scholar 

  14. Yu, W., Wang, J.: A new method to solve optimization problems via fixed point of firefly algorithm. Int. J. Bio-Inspired Comput. 11( 4), 249–256 (2018)

    Google Scholar 

  15. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  16. Storn, R., Price, K.: Differential evolution-a simple and ecient heuristic for global optimization over continuous spaces. J. Global Optim 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  17. Karaboga D., Basturk B.: A powerful and ecient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471(2007)

    Google Scholar 

  18. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization : a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)

    Article  Google Scholar 

  19. Parsajoo, M., Armaghani, D.J., Asteris, P.G.:. A precise neuro-fuzzy model en-hanced by artificial bee colony techniques for assessment of rock brittleness in-dex. Neural Comput. Appl. 34(4), 3263–3281 (2022)

    Google Scholar 

  20. Tien Bui, D., et al.: Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14(2), 447–458 (2016). https://doi.org/10.1007/s10346-016-0711-9

    Article  Google Scholar 

  21. Boudane, F., Berrichi, A.: Multi-objective artificial bee colony algorithm for parameter-free neighborhood-based clustering. Int. J. Swarm Intell. Res. (IJSIR), 12(4), 186–204 (2021)

    Google Scholar 

  22. Wu, H. , Huang, Y., Chen, L. , Zhu, Y., Li, H.: Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (nsga-ii). Environ. Res. 204, 111999 (2022)

    Google Scholar 

  23. Li, M., Wang, L., Wang, Y., Chen, Z.: Sizing optimization and energy management strategy for hybrid energy storage system using multi-objective optimization and random forests. IEEE Trans. Power Electron. 36(10), 11421–11430 (2021)

    Article  Google Scholar 

  24. Yang, G., Cao, Y., Tao, H.: A method for multi-objective optimization and application in automobile impact. J. Phys. Confer. Ser. 1802(3), 032129 (5pp) (2021)

    Google Scholar 

  25. Chogueur, O., Bentouba, S., Bourouis, M.: Modeling and optimal control applying the flower pollination algorithm to doubly fed induction generators on a wind farm in a hot arid climate. J. Sol. Energy Eng. 143(4), 1–26 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer, Pingdingshan University, Pingdingshan, 467002, China

    Xiaohui Wang

  2. College of Information Engineering, Pingdingshan University, Pingdingshan, 467002, China

    Shengpu Li

Authors
  1. Xiaohui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengpu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengpu Li .

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

Reprints 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., Li, S. (2022). A Mapping Solution Search Garden Balsam Optimization for Solving Invariant Point Problems. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09677-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09676-1

  • Online ISBN: 978-3-031-09677-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation