Skip to main content
SpringerLink
Log in

Decomposition Based Multi-objectives Evolutionary Algorithms Challenges and Circumvention

  • Conference paper
  • First Online:
Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

Included in the following conference series:

Abstract

Decomposition based Multi Objectives Evolutionary Algorithms (MOEA/D) became one of research focus in the last decade. That is due to its simplicity as well as its effectiveness in solving both constrained and unconstrained problems with different Pareto Front (PF) geometries. This paper presents the challenges on different research areas concerning MOEA/D. Firstly, the original MOEA/D algorithm is explained. Its basic idea is to decompose the Multi Objectives Optimization (MOO) problem into multiple single objective optimization sub problems and works concurrently to solve these sub problems. Each sub problem is optimized with the help of the information gained from its neighborhood. Then, two major factors that affect the search ability of decomposition based MOO algorithms: Scalarization Functions (SF) and weight vectors generation and adaptation are discussed. Furthermore, the researches in two categories of different variants of MOEA/D are illustrated. Finally, the real world application areas that applied the decomposition approach are mentioned.

A. A. A. Youssif—On leave from Faculty of Computers & Artificial Intelligence, Helwan university, Cairo, Egypt.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  2. Bui, L.T., Alam, S.: Multi Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, Hershey (2008)

    Google Scholar 

  3. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Google Scholar 

  4. Purshouse, R.C., Fleming, P.J.: On the evolutionary optimization of many conflicting objectives. IEEE Trans. Evol. Comput. 11(6), 770–784 (2007)

    Google Scholar 

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

    Google Scholar 

  6. Nebro, A.J., Durillo, J.J., García-Nieto, J., Coello, C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), Nashville, TN, USA (2009)

    Google Scholar 

  7. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Google Scholar 

  8. Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E.: Aggregation functions. part I: means. Inf. Sci. 181(1), 1–22 (2011)

    MATH  Google Scholar 

  9. Santiago, A., Huacuja, H.J.F., Dorronsoro, B., Pecero, J.E., Santillan, C.G., Barbosa, J.J.G., Monterrubio, J.C.S.: A survey of decomposition methods for multi-objective optimization. In: Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Springer, vol. 547, pp. 453–465, 2014

    Google Scholar 

  10. Miettinen, K., Makela, M.M.: On scalarizing functions in multiobjective optimization. Oper. Res. Spektrum 24, 193–213 (2002)

    MathSciNet  MATH  Google Scholar 

  11. Jiang, S., Yang, S., Wang, Y., Liu, X.: Scalarizing functions in decomposition-based multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 22(2), 296–313 (2018)

    Google Scholar 

  12. Ishibuchi, H., Sakane, Y., Tsukamoto, N., Nojima, Y.: Simultaneous use of different scalarizing functions in MOEA/D. In: GECCO 2010, Portland, Oregon, USA (2010)

    Google Scholar 

  13. Pescador-Rojas, M., Coello, C.A.C.: Collaborative and adaptive strategies of different scalarizing functions in MOEA/D. In: IEEE Congress on Evolutionary Computation (CEC) (2018)

    Google Scholar 

  14. Ma, X., Zhang, Q., Tian, G., Yang, J., Zhu, Z.: On Tchebycheff decomposition approaches for multi-objective evolutionary optimization. IEEE Trans. Evol. Comput. 22(2), 226–244 (2018)

    Google Scholar 

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

    Google Scholar 

  16. Siwei, J., Zhihua, C., Jie, Z., Yew-Soon, O.: Multiobjective optimization by decomposition with Pareto-adaptive weight vectors. In: Seventh International Conference on Natural Computation, Shanghai, China (2011)

    Google Scholar 

  17. Guo, X., Wang, X., Wei, Z.: MOEA/D with adaptive weight vector design. In: 11th International Conference on Computational Intelligence and Security, Shenzhen, China (2015)

    Google Scholar 

  18. Farias, L.R.C.d., Braga, P.H.M., Bassani, H.F., Araújo, A.F.R.: MOEA/D with uniformly randomly adaptive weights. In: GECCO 2018, Kyoto, Japan (2018)

    Google Scholar 

  19. Zhang, C., Tan, K.C., Lee, L.H., Gao, L.: Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts. Soft Comput. 22(12), 3997–4012 (2018)

    Google Scholar 

  20. Gu, F., Cheung, Y.-M.: Self-organizing map-based weight design for decomposition-based many-objective evolutionary algorithm. IEEE Trans. Evol. Comput. 22(2), 211–225 (2018)

    Google Scholar 

  21. Meneghini, I.R., Guimaraes, F.G.: Evolutionary method for weight vector generation in multi-objective evolutionary algorithms based on decomposition and aggregation. In: CEC, San Sebastián (2017)

    Google Scholar 

  22. Castro, O.R., Santana, R., Lozano, J.A., Pozo, A.: Combining CMA-ES and MOEA/DD for many-objective optimization. In: IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain (2017)

    Google Scholar 

  23. Zapotecas-Martínez, S., Moraglio, A., Aguirre, H., Tanaka, K.: Geometric particle swarm optimization for multi-objective optimization using decomposition. In: GECCO 2016, Denver, CO, USA (2016)

    Google Scholar 

  24. Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans. Cybern. 43(6), 1845–1859 (2013)

    Google Scholar 

  25. Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)

    Google Scholar 

  26. Xu, H., Zeng, W., Zhang, D., Zeng, X.: MOEA/HD: a multiobjective evolutionary algorithm based on hierarchical decomposition. IEEE Trans. Cybern. 49(2), 517–526 (2019)

    Google Scholar 

  27. Zhang, Q., Li, H., Maringer, D., Tsang, E.: MOEA/D with NBI-style Tchebycheff approach for Portfolio Management. In: IEEE Congress on Evolutionary Computation, Barcelona, Spain (2010)

    Google Scholar 

  28. Xing, H., Wang, Z., Li, T., Li, H.: An improved MOEA/D algorithm for multi-objective multicast routing with network coding. Appl. Soft Comput. 59, 88–103 (2017)

    Google Scholar 

  29. Gunantara, N.: A review of multi-objective optimization: methods and its applications. Cogent Eng. 5(1) (2018). https://doi.org/10.1080/23311916.2018.1502242

  30. Emmerich, M.T.M.: A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Num. Comput. 17(3), 585–609 (2018)

    MathSciNet  Google Scholar 

  31. Trivedi, A., Srinivasan, D., Sanyal, K., Ghosh, A.: A survey of multi-objective evolutionary algorithms based on decomposition. IEEE Trans. Evol. Comput. 21(3), 440–462 (2017)

    Google Scholar 

  32. Messac, A., Mattson, C.A.: Normal constraint method with guarantee of even representation of complete Pareto frontier. AIAA J. 42(10), 2101–2111 (2004)

    Google Scholar 

  33. Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)

    MathSciNet  MATH  Google Scholar 

  34. Li, H., Ding, M., Deng, J., Zhang, Q.: On the use of random weights in MOEA/D. In: 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan (2015)

    Google Scholar 

  35. Qi, Y., Ma, X., Liu, F., Jiao, L., Sun, J., Wu, J.: MOEA/D with adaptive weight adjustment. Evol. Comput. 22(2), 231–264 (2013)

    Google Scholar 

  36. Wu, M., Kwong, S., Jia, Y., Li, K., Zhang, Q.: Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression. In: GECCO 2017 Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany (2017)

    Google Scholar 

  37. Qi, Y., Ma, X., Liu, F., Jiao, L., Sun, J., Wu, J.: MOEA/D with adaptive weight adjustment. Evol. Comput. 22(2), 231–264 (2014)

    Google Scholar 

  38. Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2015)

    Google Scholar 

  39. Zapotecas-Martínez, S., Derbel, B., Brockhoff, D., Aguirre, H.E., Tanaka, K.: Injecting CMA-ES into MOEA/D. In: GECCO 2015, Madrid, Spain (2015)

    Google Scholar 

  40. Hansen, N., Auger, A.: CMA-ES: evolution strategies and covariance matrix adaptation. In: GECCO 2011, Dublin, Ireland (2011)

    Google Scholar 

  41. Moraglio, A., Chio, C.D., Poli, R.: Geometric particle swarm optimisation. In: Genetic Programming, EuroGP 2007. Lecture Notes in Computer Science, vol. 4445. Springer, Heidelberg (2007)

    Google Scholar 

  42. Jiang, S., Yang, S.: An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts. IEEE Trans. Cybern. 46(2), 421–437 (2016)

    Google Scholar 

  43. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report. 103, Zurich, Switzerland (2001)

    Google Scholar 

  44. Li, M., Yang, S., Liu, X.: Shift-based density estimation for pareto-based algorithms in many-objective optimization. IEEE Trans. Evol. Comput. 18(3), 348–365 (2014)

    Google Scholar 

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

    Google Scholar 

  46. Sarkar, S., Das, S., Chaudhuri, S.S.: Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl. Soft Comput. 50, 142–157 (2017)

    Google Scholar 

  47. Liu, B., Fernández, F.V., Zhang, Q., Pak, M., Sipahi, S., Gielen, G.: An enhanced MOEA/D-DE and its application to multiobjective analog cell sizing. In: IEEE Congress on Evolutionary Computation, Barcelona, Spain (2010)

    Google Scholar 

  48. Ho-Huu, V., Hartjes, S., Visser, H.G., Curran, R.: An efficient application of the MOEA/D algorithm for designing noise abatement departure trajectories. Aerospace 4(4), 54 (2017)

    Google Scholar 

  49. Qi, Y., Bao, L., Ma, X., Maio, Q.: Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: a case study on reservoir flood control operation. Inf. Sci. 367(10), 529–549 (2016)

    Google Scholar 

  50. Li, L., Chen, H., Li, J., Jing, N., Emmerich, A.M.: Preference-based evolutionary many-objective optimization for agile satellite mission planning. IEEE Access 6, 40963–40978 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt

    Sherin M. Omran, Wessam H. El-Behaidy & Aliaa A. A. Youssif

  2. College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt

    Aliaa A. A. Youssif

Authors
  1. Sherin M. Omran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wessam H. El-Behaidy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aliaa A. A. Youssif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sherin M. Omran .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Omran, S.M., El-Behaidy, W.H., Youssif, A.A.A. (2020). Decomposition Based Multi-objectives Evolutionary Algorithms Challenges and Circumvention. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_6

Download citation

Publish with us

Policies and ethics

Search

Navigation