[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A Large-Scale Multi-objective Brain Storm Optimization Algorithm Based on Direction Vectors and Variance Analysis

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13968))

Included in the following conference series:

Abstract

Large-scale multi-objective optimization problems (LSMOPs) can lead to the conventional reproduction operator being inefficient for searching. Therefore, we propose a large-scale multi-objective brain storm optimization algorithm based on direction vectors and variance analysis (LMOBSO-DV) to enhance the efficiency of tackling LSMOPs. Specifically, we adopt brain storm optimization (BSO) algorithm using reference vectors to divide the population into subpopulations and guide the individuals i) in each subpopulation to search in promising directions and 2) between subpopulations to maintain diversity. We also design a new mutation operator. On a widely used LSMOPs test suites with 1000 decision variables, 2 objectives, and 3 objectives, we evaluate LMOBSO-DV’s effectiveness in comparison to other several state-of-the-art algorithms. The results of the experiment show that our proposed approach, LMOBSO-DV, outperforms the other studied algorithms.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 55.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 69.99
Price includes VAT (United Kingdom)
  • 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. Abdi, Y., Feizi-Derakhshi, M.R.: Hybrid multi-objective evolutionary algorithm based on search manager framework for big data optimization problems. Appl. Soft Comput. 87, 105991 (2020)

    Article  Google Scholar 

  2. Antonio, L.M., Coello, C.A.C.: Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2758–2765. IEEE (2013)

    Google Scholar 

  3. Chen, H., Zhu, Y., Hu, K., Ma, L.: Bacterial colony foraging algorithm: combining chemotaxis, cell-to-cell communication, and self-adaptive strategy. Inf. Sci. 273, 73–100 (2014)

    Article  MathSciNet  Google Scholar 

  4. Cheng, R., Jin, Y., Narukawa, K., Sendhoff, B.: A multiobjective evolutionary algorithm using gaussian process-based inverse modeling. IEEE Trans. Evol. Comput. 19(6), 838–856 (2015)

    Article  Google Scholar 

  5. Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016)

    Article  Google Scholar 

  6. Cheng, R., Jin, Y., Olhofer, M., et al.: Test problems for large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. 47(12), 4108–4121 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. He, C., Cheng, R., Tian, Y., Zhang, X.: Iterated problem reformulation for evolutionary large-scale multiobjective optimization. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)

    Google Scholar 

  9. He, C., Cheng, R., Yazdani, D.: Adaptive offspring generation for evolutionary large-scale multiobjective optimization. IEEE Trans. Syst. Man Cybern. Syst. 52(2), 786–798 (2020)

    Article  Google Scholar 

  10. He, C., Huang, S., Cheng, R., Tan, K.C., Jin, Y.: Evolutionary multiobjective optimization driven by generative adversarial networks (GANs). IEEE Trans. Cybern. 51(6), 3129–3142 (2020)

    Article  Google Scholar 

  11. He, C., et al.: Accelerating large-scale multiobjective optimization via problem reformulation. IEEE Trans. Evol. Comput. 23(6), 949–961 (2019)

    Article  Google Scholar 

  12. Hong, W.J., Yang, P., Tang, K.: Evolutionary computation for large-scale multi-objective optimization: a decade of progresses. Int. J. Autom. Comput. 18(2), 155–169 (2021)

    Article  Google Scholar 

  13. Li, M., Wei, J.: A cooperative co-evolutionary algorithm for large-scale multi-objective optimization problems. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1716–1721 (2018)

    Google Scholar 

  14. Li, N., Ma, L., Yu, G., Xue, B., Zhang, M., Jin, Y.: Survey on evolutionary deep learning: Principles, algorithms, applications and open issues. arXiv preprint arXiv:2208.10658 (2022)

  15. Liu, R., Liu, J., Li, Y., Liu, J.: A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization problems. Swarm Evol. Comput. 55, 100684 (2020)

    Article  Google Scholar 

  16. Liu, R., Ren, R., Liu, J., Liu, J.: A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems. Appl. Soft Comput. 89, 106120 (2020)

    Article  Google Scholar 

  17. Liu, S., Lin, Q., Wong, K.C., Li, Q., Tan, K.C.: Evolutionary large-scale multiobjective optimization: Benchmarks and algorithms. IEEE Trans. Evol. Comput. (2021)

    Google Scholar 

  18. Ma, L., Cheng, S., Shi, Y.: Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans. Syst. Man Cybern. Syst. 51(11), 6723–6742 (2020)

    Article  Google Scholar 

  19. Ma, L., Hu, K., Zhu, Y., Niu, B., Chen, H., He, M.: Discrete and continuous optimization based on hierarchical artificial bee colony optimizer. J. Appl. Math. 2014 (2014)

    Google Scholar 

  20. Ma, L., Huang, M., Yang, S., Wang, R., Wang, X.: An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. 52(7), 6684–6696 (2021)

    Article  Google Scholar 

  21. Ma, L., et al.: Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Trans. Cybern. (2021)

    Google Scholar 

  22. Ma, L., Wang, X., Huang, M., Lin, Z., Tian, L., Chen, H.: Two-level master-slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Trans. Syst. Man Cybern. Syst. 49(5), 861–880 (2017)

    Article  Google Scholar 

  23. Ma, L., Wang, X., Huang, M., Zhang, H., Chen, H.: A novel evolutionary root system growth algorithm for solving multi-objective optimization problems. Appl. Soft Comput. 57, 379–398 (2017)

    Article  Google Scholar 

  24. Ma, X., et al.: A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Trans. Evol. Comput. 20(2), 275–298 (2015)

    Article  Google Scholar 

  25. Miguel Antonio, L., Coello Coello, C.A.: Decomposition-based approach for solving large scale multi-objective problems. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 525–534. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_49

    Chapter  Google Scholar 

  26. 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 

  27. Shi, Y.: Brain storm optimization algorithm in objective space. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1227–1234. IEEE (2015)

    Google Scholar 

  28. Shi, Y., Xue, J., Wu, Y.: Multi-objective optimization based on brain storm optimization algorithm. Int. J. Swarm Intell. Res. (IJSIR) 4(3), 1–21 (2013)

    Article  Google Scholar 

  29. Song, A., Yang, Q., Chen, W.N., Zhang, J.: A random-based dynamic grouping strategy for large scale multi-objective optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 468–475. IEEE (2016)

    Google Scholar 

  30. Tian, Y., Cheng, R., Zhang, X., Jin, Y.: Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)

    Article  Google Scholar 

  31. Tian, Y., Lu, C., Zhang, X., Tan, K.C., Jin, Y.: Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Trans. Cybern. 51(6), 3115–3128 (2020)

    Article  Google Scholar 

  32. Tian, Y., et al.: Evolutionary large-scale multi-objective optimization: a survey. ACM Comput. Surv. (CSUR) 54(8), 1–34 (2021)

    MathSciNet  Google Scholar 

  33. Tian, Y., Zheng, X., Zhang, X., Jin, Y.: Efficient large-scale multiobjective optimization based on a competitive swarm optimizer. IEEE Trans. Cybern. 50(8), 3696–3708 (2019)

    Article  Google Scholar 

  34. Zeng, R., Su, M., Yu, R., Wang, X.: CD\(^2\): fine-grained 3D mesh reconstruction with twice chamfer distance. ACM Trans. Multimedia Comput. Commun. Appl. (2023). https://doi.org/10.1145/3582694

  35. Zhang, B., Wang, X., Ma, L., Huang, M.: Optimal controller placement problem in internet-oriented software defined network. In: 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 481–488. IEEE (2016)

    Google Scholar 

  36. Zhang, X., Tian, Y., Cheng, R., Jin, Y.: A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 97–112 (2016)

    Article  Google Scholar 

  37. Zhang, Y., Wang, G.G., Li, K., Yeh, W.C., Jian, M., Dong, J.: Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Inf. Sci. 522, 1–16 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  38. Zille, H., Ishibuchi, H., Mostaghim, S., Nojima, Y.: Weighted optimization framework for large-scale multi-objective optimization. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 83–84 (2016)

    Google Scholar 

  39. Zille, H., Ishibuchi, H., Mostaghim, S., Nojima, Y.: A framework for large-scale multiobjective optimization based on problem transformation. IEEE Trans. Evol. Comput. 22(2), 260–275 (2017)

    Article  Google Scholar 

  40. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Fundamental Re-search Funds for the Central Universities No. N2117005, the Joint Funds of the Natural Science Foundation of Liaoning Province und Grant 2021-KF-11-01 and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

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

    Yang Liu, Yuee Zhou, Nan Li & Lianbo Ma

  2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

    Lianbo Ma

  3. Neusoft Institute of Intelligent Medical Research, Shenyang, 110167, China

    Tiejun Xing

  4. School of Computer Science and Engineering Northeastern University, Shenyang, 110167, China

    Yingyou Wen

  5. Digital Factory Department, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, 110016, China

    Chang Liu & Haibo Shi

Authors
  1. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tiejun Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuee Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lianbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yingyou Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Haibo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Tiejun Xing or Lianbo Ma .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Harbin Institute of Technology, Shenzhen, China

    Wenjian Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y. et al. (2023). A Large-Scale Multi-objective Brain Storm Optimization Algorithm Based on Direction Vectors and Variance Analysis. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36622-2_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36621-5

  • Online ISBN: 978-3-031-36622-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation