Skip to main content
SpringerLink
Log in

A Surrogate-Assisted Improved Many-Objective Evolutionary Algorithm

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11656))

Included in the following conference series:

  • 864 Accesses

Abstract

The many-objective evolutionary algorithm is an effective method to tackle many-objective optimization problems. We improve the two-archive2 algorithm (Two_Arch2) by adopting the Levy distribution and opposition-based learning strategy. In addition, we propose a hybrid adaptive strategy of the surrogate models. The criterion for evaluating the model quality is developed. In model management, selection of individuals is based on the criterion named angle penalized distance (APD). In the experiments, we make comparisons of the IGD among our algorithm and the other algorithms on the DTLZ and MaF test suites, which exhibits the superiority of the improved algorithm.

Supported by organization in part by the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University under Grant 2018002, in part by the Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education under Grant No. MSC-201602A.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Segredo, E., Luque, G., Segura, C., Alba, E.: Optimising real-world traffic cycle programs by using evolutionary computation. IEEE Access 7, 43915–43932 (2019)

    Article  Google Scholar 

  2. Herrero, J.G., Berlanga, A., Lopez, J.M.M.: Effective evolutionary algorithms for many-specifications attainment: application to air traffic control tracking filters. IEEE Tran. Evol. Comput. 13(1), 151–168 (2009)

    Article  Google Scholar 

  3. Liu, C., Zhao, Q., Yan, B., Elsayed, S., Ray, T., Sarker, R.: Adaptive sorting-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 23, 247–257 (2019)

    Article  Google Scholar 

  4. Fleming, P.J., Purshouse, R.C., Lygoe, R.J.: Many-objective optimization: an engineering design perspective. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 14–32. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_2

    Chapter  MATH  Google Scholar 

  5. Yuan, Y., Xu, H., Wang, B., Yao, X.: A new dominance relation-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(1), 16–37 (2016)

    Article  Google Scholar 

  6. 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 

  7. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  8. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)

    Article  Google Scholar 

  9. Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol. Comput. 1(2), 61–70 (2011)

    Article  Google Scholar 

  10. Ohno, M., Yoshimatsu, A., Kobayashi, M., Watanabe, S.: A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5), 481–494 (2002)

    Article  Google Scholar 

  11. Lim, D., Jin, Y., Ong, Y.S., Sendhoff, B.: Generalizing surrogate-assisted evolutionary computation. IEEE Trans. Evol. Comput. 14(3), 329–355 (2010)

    Article  Google Scholar 

  12. Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)

    Article  Google Scholar 

  13. Zhang, Q., Liu, W., Tsang, E., Virginas, B.: Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans. Evol. Comput. 14(3), 456–474 (2010)

    Article  Google Scholar 

  14. Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., Sindhya, K.: A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 129–142 (2018)

    Article  Google Scholar 

  15. Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., Jin, Y.: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23, 74–88 (2019)

    Article  Google Scholar 

  16. Wang, H., Jiao, L., Yao, X.: Two\(\_\)arch2: an improved two-archive algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(4), 524–541 (2015)

    Article  Google Scholar 

  17. Guo, D., Jin, Y., Ding, J., Chai, T.: Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems. IEEE Trans. Cybern. PP(99), 1–14 (2018)

    Article  Google Scholar 

  18. Reynolds, A.: Liberating lévy walk research from the shackles of optimal foraging. Phys. Life Rev. 14, 59–83 (2015)

    Article  Google Scholar 

  19. Schroeder, A., Ramakrishnan, S., Kumar, M., Trease, B.: Efficient spatial coverage by a robot swarm based on an ant foraging model and the lévy distribution. Swarm Intell. 11(1), 39–69 (2017)

    Article  Google Scholar 

  20. Al-Qunaieer, F.S., Tizhoosh, H.R., Rahnamayan, S.: Opposition based computing — a survey. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, July 2010

    Google Scholar 

  21. Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181(20), 4699–4714 (2011)

    Article  MathSciNet  Google Scholar 

  22. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_84

    Chapter  Google Scholar 

  23. Zolan, A.J., Hasenbein, J.J., Morton, D.P.: Optimizing the design of a Latin hypercube sampling estimator. In: 2017 Winter Simulation Conference (WSC), pp. 1832–1843, December 2017

    Google Scholar 

  24. Zhang, J., Zhou, A., Zhang, G.: A classification and pareto domination based multiobjective evolutionary algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2883–2890, May 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hebei University of Technology, Tianjin, 300401, China

    Bin Cao, Yi Su & Shanshan Fan

Authors
  1. Bin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shanshan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Cao .

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

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, B., Su, Y., Fan, S. (2019). A Surrogate-Assisted Improved Many-Objective Evolutionary Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26354-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26353-9

  • Online ISBN: 978-3-030-26354-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation