Skip to main content
SpringerLink
Log in

Brain storm optimization algorithm for solving knowledge spillover problems

  • S.I. : New Trends of Neural Computing for Advanced Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Adami C, Schossau J, Hintze A (2016) Evolutionary game theory using agent-based methods. Phys Life Rev 19:1–26. https://doi.org/10.1016/j.plrev.2016.08.015

    Article  Google Scholar 

  2. Ao N, Zhao M, Li Q, Qu S, Zhou W (2020) Network characteristics for neighborhood field algorithms. Neural Comput Appl 32:12061–12078. https://doi.org/10.1007/s00521-019-04255-0

    Article  Google Scholar 

  3. Azuaje F (2003) A computational evolutionary approach to evolving game strategy and cooperation. IEEE Trans Syst Man Cybern Part B (Cybern) 33(3):498–503. https://doi.org/10.1109/TSMCB.2003.810948

    Article  Google Scholar 

  4. Ceccagnoli M, Forman C, Huang P, Wu DJ (2014) Digital platforms: when is participation valuable? Commun ACM 57(2):38–39. https://doi.org/10.1145/2556940

    Article  Google Scholar 

  5. Cheng S, Lei X, Lu H, Zhang Y, Shi Y (2019) Generalized pigeon-inspired optimization algorithms. Sci China Inf Sci 62:070211:1–070211:3. https://doi.org/10.1007/s11432-018-9727-y

    Article  MathSciNet  Google Scholar 

  6. Cheng S, Lu H, Lei X, Shi Y (2018) A quarter century of particle swarm optimization. Compl Intell Syst 4(3):227–239. https://doi.org/10.1007/s40747-018-0071-2

    Article  Google Scholar 

  7. Cheng S, Ma L, Lu H, Lei X, Shi Y (2020) Evolutionary computation for solving search-based data analytics problems. Artificial Intelligence Review p. in press. https://doi.org/10.1007/s10462-020-09882-x

  8. Cheng S, Qin Q, Chen J, Shi Y (2016) Brain storm optimization algorithm: a review. Artif Intell Rev 46(4):445–458. https://doi.org/10.1007/s10462-016-9471-0

    Article  Google Scholar 

  9. Cheng S, Shi Y (2019) Brain storm optimization algorithms: concepts, principles and applications, adaptation, learning, and optimization. Springer International Publishing AG, Berlin. https://doi.org/10.1007/978-3-030-15070-9

    Book  MATH  Google Scholar 

  10. Cressman R, Apaloo J (2018) Evolutionary game theory. In: Başar T, Zaccour G (eds) Handbook of dynamic game theory. Springer International Publishing, Cham, pp 461–510. https://doi.org/10.1007/978-3-319-44374-4_6

    Chapter  Google Scholar 

  11. Cuvero M, Granados ML, Pilkington A, Evans RD (2019) The effects of knowledge spillovers and accelerator programs on the product innovation of high-tech start-ups: a multiple case study. IEEE Trans Eng Manag pp 1–14. https://doi.org/10.1109/TEM.2019.2923250

  12. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  13. Duan H, Qiao P (2014) Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. J Intell Comput Cybern 7(1):24–37. https://doi.org/10.1108/IJICC-02-2014-0005

    Article  MathSciNet  Google Scholar 

  14. El-Abd M (2017) Global-best brain storm optimization algorithm. Swarm Evol Comput 37:27–44. https://doi.org/10.1016/j.swevo.2017.05.001

    Article  Google Scholar 

  15. García-Ródenas R, Linares LJ, López-Gómez JA (2020) Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm. Neural Comput Appl in press. https://doi.org/10.1007/s00521-020-05131-y

  16. Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publisher, San Francisco

    Google Scholar 

  17. Lee PC (2019) Investigating the knowledge spillover and externality of technology standards based on patent data. IEEE Trans Eng Manag pp 1–15. https://doi.org/10.1109/TEM.2019.2911636

  18. Lozito GM, Salvini A (2020) Swarm intelligence based approach for efficient training of regressive neural networks. Neural Comput Appl 32:10693–10704. https://doi.org/10.1007/s00521-019-04606-x

    Article  Google Scholar 

  19. Ma L, Cheng S, Shi Y (2020) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst https://doi.org/10.1109/TSMC.2020.2963943

  20. Miȩkisz J (2008) Evolutionary game theory and population dynamics. In: Capasso V, Lachowicz M (eds) Multiscale problems in the life sciences: from microscopic to macroscopic. Springer, Berlin, pp 269–316. https://doi.org/10.1007/978-3-540-78362-6_5

    Chapter  Google Scholar 

  21. Newton J (2018) Evolutionary game theory: a renaissance. Games 9(2):1–67. https://doi.org/10.3390/g9020031

    Article  MathSciNet  MATH  Google Scholar 

  22. Olafsson S (1996) Resource allocation as an evolving strategy. Evol Comput 4(1):33–55. https://doi.org/10.1162/evco.1996.4.1.33

    Article  Google Scholar 

  23. Phelps S, Wooldridge M (2013) Game theory and evolution. IEEE Intell Syst 28(4):76–81. https://doi.org/10.1109/MIS.2013.110

    Article  Google Scholar 

  24. Rothaermel FT, Ku DN (2008) Intercluster innovation differentials: the role of research universities. IEEE Trans Eng Manag 55(1):9–22. https://doi.org/10.1109/TEM.2007.912815

    Article  Google Scholar 

  25. Sandholm WH (2010) Population games and evolutionary dynamics. Economic learning and social evolution. MIT Press, Cambridge

    MATH  Google Scholar 

  26. Shi Y (2011) An optimization algorithm based on brainstorming process. Int J Swarm Intell Res (IJSIR) 2(4):35–62. https://doi.org/10.4018/jsir.2011100103

    Article  Google Scholar 

  27. Shi Y (2015) Brain storm optimization algorithm in objective space. In: Proceedings of 2015 IEEE congress on evolutionary computation (CEC 2015), pp 1227–1234. IEEE, Sendai, Japan. https://doi.org/10.1109/CEC.2015.7257029

  28. Shi Y (2018) Unified swarm intelligence algorithms. In: Shi Y (ed) Critical developments and applications of swarm intelligence. IGI Global, Hershey, pp 1–26. https://doi.org/10.4018/978-1-5225-5134-8.ch001

    Chapter  Google Scholar 

  29. Wang R, Ishibuchi H, Zhou Z, Liao T, Zhang T (2018) Localized weighted sum method for many-objective optimization. IEEE Trans Evol Comput 22(1):3–18

    Article  Google Scholar 

  30. Wang R, Zhang Q, Zhang T (2016) Decomposition based algorithms using Pareto adaptive scalarizing methods. IEEE Trans Evol Comput 20(6):821–837

    Article  Google Scholar 

  31. Wei P, Botang H, Lei H, Xiao L (2018) Research on knowledge spillover model of urban agglomeration under the background of informatization. In: Proceedings of the 4th international conference on frontiers of educational technologies (ICFET 2018), pp 142–146. ACM. https://doi.org/10.1145/3233347.3233383

  32. Xiao J, Andelfinger P, Eckhoff D, Cai W, Knoll A (2019) A survey on agent-based simulation using hardware accelerators. ACM Comput Surv 51(6):131:1–131:35. https://doi.org/10.1145/3291048

    Article  Google Scholar 

  33. Xu J, Huang E, Chen CH, Lee LH (2015) Simulation optimization: a review and exploration in the new era of cloud computing and big data. Asia-Pac J Oper Res 32(3):1–34. https://doi.org/10.1142/S0217595915500190

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhang X, Lu X, Zhang X, Wang L (2020) A novel three-coil wireless power transfer system and its optimization for implantable biomedical applications. Neural Comput Appl 32:7069–7078. https://doi.org/10.1007/s00521-019-04214-9

    Article  Google Scholar 

  35. Zhang X, Zhang X, Han L (2019) An energy efficient internet of things network using restart artificial bee colony and wireless power transfer. IEEE Access 7:12686–12695. https://doi.org/10.1109/ACCESS.2019.2892798

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by National Natural Science Foundation of China (Grant Nos. 61806119, 61672334, 61761136008, 61703256, and 61773103), Natural Science Basic Research Plan In Shaanxi Province of China (No. 2019JM-320), Fundamental Research Funds for the Central Universities (Nos. GK202003078, GK201803020), and Graduate innovation team project of Shaanxi Normal University (No. TD2020014Z).

Author information

Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Shi Cheng & Mingming Zhang

  2. College of Software, Northeastern University, Shenyang, 110819, China

    Lianbo Ma

  3. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Hui Lu

  4. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China

    Rui Wang

  5. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuhui Shi

Authors
  1. Shi Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lianbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuhui Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shi Cheng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, S., Zhang, M., Ma, L. et al. Brain storm optimization algorithm for solving knowledge spillover problems. Neural Comput & Applic 35, 12247–12260 (2023). https://doi.org/10.1007/s00521-020-05674-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05674-0

Keywords

Search

Navigation