Skip to main content
SpringerLink
Log in

Multi-Factorial Evolutionary Algorithm Based on M2M Decomposition

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Included in the following conference series:

Abstract

This paper proposes a decomposition-based multi-objective multi-factorial evolutionary algorithm (MFEA/D-M2M). The MFEA/D-M2M adopts the M2M approach to decompose multi-objective optimization problems into multiple constrained sub-problems for enhancing the diversity of population and convergence of sub-regions. An machine learning model augmented version is also been implemented, which utilized discriminative models for pre-selecting solutions. Experimental studies on nine multi-factorial optimization (MFO) problem sets are conducted. The experimental results demonstrated that MFEA/D-M2M outperforms the vanilla MFEA on six MFO benchmark problem sets and achieved comparable results on the other three problem sets with partial intersection of global optimal.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Yuan, Y., Ong, Y.-S., Feng, L., Qin, A.K., Gupta, A., Da, B., Zhang, Q., Tan, K.C., Jin, Y., Ishibuchi, H.: Evolutionary multitasking for multi-objective continuous optimization: benchmark problems, performance metrics and baseline results. Technical report, Nanyang Technological University (2016)

    Google Scholar 

  2. Back, T., Hammel, U., Schwefel, H.-P.: Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1(1), 3–17 (1997)

    Article  Google Scholar 

  3. Gupta, A., Ong, Y.-S., Da, B., Feng, L., Handoko, S.D.: Landscape synergy in evolutionary multitasking. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3076–3083, July 2016

    Google Scholar 

  4. Da, B., Gupta, A., Ong, Y.-S., Feng, L.: Evolutionary multitasking across single and multi-objective formulations for improved problem solving. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1695–1701. IEEE (2016)

    Google Scholar 

  5. Gupta, A., Ong, Y.-S., Da, B., Feng, L., Handoko, S.D.: Landscape synergy in evolutionary multitasking. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3076–3083. IEEE (2016)

    Google Scholar 

  6. Gupta, A., Ong, Y.-S.: Genetic transfer or population diversification? Deciphering the secret ingredients of evolutionary multitask optimization. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7 (2016)

    Google Scholar 

  7. Gupta, A., Ong, Y.-S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)

    Article  Google Scholar 

  8. Gupta, A., Ong, Y.-S., Feng, L., Tan, K.C.: Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans. Cybern. 47(7), 1652–1665 (2017)

    Article  Google Scholar 

  9. Zhou, L., Feng, L., Zhong, J., Ong, Y.-S., Zhu, Z., Sha, E.: Evolutionary multitasking in combinatorial search spaces: a case study in capacitated vehicle routing problem. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016)

    Google Scholar 

  10. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  11. Ong, Y.-S., Gupta, A.: Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn. Comput. 8(2), 125–142 (2016)

    Article  Google Scholar 

  12. Agrawal, R.B., Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  13. Liu, H.-L., Gu, F., Zhang, Q.: Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Trans. Evol. Comput. 18(3), 450–455 (2014)

    Article  Google Scholar 

  14. Lin, X., Zhang, Q., Kwong, S.: A decomposition based multiobjective evolutionary algorithm with classification. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3292–3299. IEEE (2016)

    Google Scholar 

  15. Da, B., Ong, Y.-S., Feng, L., Qin, A.K., Gupta, A., Zhu, Z., Ting, C.-K., Tang, K., Yao, X.: Evolutionary multitasking for single-objective continuous optimization: benchmark problems, performance metric, and baseline results. Nanyang Technological University, Singapore, Technical report (2016)

    Google Scholar 

  16. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithm research: a history and analysis. Technical report, Citeseer (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou, 515063, China

    Zhun Fan

  2. Department of Electronic Engineering, Shantou University, Shantou, 515063, China

    Jiajie Mo, Zhun Fan, Wenji Li, Yi Fang & Yugen You

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 210016, China

    Xinye Cai

Authors
  1. Jiajie Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhun Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenji Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yugen You
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xinye Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhun Fan .

Editor information

Editors and Affiliations

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

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

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

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mo, J., Fan, Z., Li, W., Fang, Y., You, Y., Cai, X. (2017). Multi-Factorial Evolutionary Algorithm Based on M2M Decomposition. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68759-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation