Skip to main content
SpringerLink
Log in

A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness of the proposed framework, the performances of the combined algorithms of five CHTs and four MOEAs on eight constrained multi-objective optimization problems are investigated with the proposed framework. The experimental results show that the outperforming CHT can vary by constrained multi-objective optimization problems, as far as examined in this study.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), 13 (2015)

    Article  Google Scholar 

  2. Mezura-Montes, E., Coello Coello, C.A.: Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011)

    Article  Google Scholar 

  3. Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE TEVC 18(4), 602–622 (2014)

    Google Scholar 

  4. Asafuddoula, M., Ray, T., Sarker, R.A.: A decomposition-based evolutionary algorithm for many objective optimization. IEEE TEVC 19(3), 445–460 (2015)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  7. Mallipeddi, R., Suganthan, P.N.: Ensemble of constraint handling techniques. IEEE TEVC 14(4), 561–579 (2010)

    Google Scholar 

  8. Rodrigues, M.D.C., de Lima, B.S.L.P., Guimarães, S.: Balanced ranking method for constrained optimization problems using evolutionary algorithms. Inf. Sci. 327(C), 71–90 (2016)

    Article  Google Scholar 

  9. de Paula Garcia, R., de Lima, B.S.L.P., de Castro Lemonge, A.C., Jacob, B.P.: A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms. Comput. Struct. 187(Supplement), 77–87 (2017)

    Article  Google Scholar 

  10. Jordehi, A.R.: A review on constraint handling strategies in particle swarm optimisation. Neural Comput. Appl. 26(6), 1265–1275 (2015)

    Article  Google Scholar 

  11. Courant, R.: Variational methods for the solution of problems of equilibrium and vibrations. Bull. Amer. Math. Soc. 49(1), 1–23 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  12. Homaifar, A., Lai, S.H., Qi, X.: Constrained optimization via genetic algorithm. Simulation 62(4), 242–254 (1994)

    Article  Google Scholar 

  13. Coello Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11), 1245–1287 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Tessema, B., Yen, G.G.: A self adaptive penalty function based algorithm for constrained optimization. In: IEEE CEC, pp. 246–253 (2006)

    Google Scholar 

  15. Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K.: Adaptive evolutionary planner/navigator for mobile robots. IEEE TEVC 1, 18–28 (1997)

    Google Scholar 

  16. Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2), 311–338 (2000)

    Article  MATH  Google Scholar 

  17. Takahama, T., Sakai, S.: Constrained optimization by the \(\varepsilon \) constrained differential evolution with gradient-based mutation and feasible elites. In: IEEE CEC, pp. 246–253 (2006)

    Google Scholar 

  18. Mezura-Montes, E., Coello Coello, C.A., Tun-Morales, E.I.: Simple feasibility rules and differential evolution for constrained optimization. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 707–716. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24694-7_73

    Chapter  Google Scholar 

  19. Paredis, J.: Co-evolutionary constraint satisfaction. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 46–55. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_249

    Chapter  Google Scholar 

  20. Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE TEVC 4(3), 284–294 (2000)

    Google Scholar 

  21. Adeli, H., Cheng, N.T.: Augmented lagrangian genetic algorithm for structural optimization. J. Aerosp. Eng. 7, 104–118 (1994)

    Article  Google Scholar 

  22. Le, T.V.: A fuzzy evolutionary approach to solving constraint problems. In: IEEE CEC, vol. 1, pp. 317–319 (1995)

    Google Scholar 

  23. Jan, M.A., Tairan, N.M., Khanum, R.A., Mashwani, W.K.: A new threshold based penalty function embedded MOEA/D. Int. J. Adv. Comput. Sci. Appl. 7(2), 645–655 (2016)

    Google Scholar 

  24. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TEVC 6(2), 182–197 (2002)

    Google Scholar 

  25. Fan, Z., Li, H., Wei, C., Li, W., Huang, H., Cai, X., Cai, Z.: An improved epsilon constraint handling method embedded in MOEA/D for constrained multi-objective optimization problems. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016)

    Google Scholar 

  26. Singh, H.K., Ray, T., Sarker, R.A.: Optimum oil production planning using infeasibility driven evolutionary algorithm. Evol. Comput. 21(1), 65–82 (2013)

    Article  Google Scholar 

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

    Google Scholar 

  28. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (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 

  29. Jaimes, A.L., Oyama, A., Fujii, K.: A ranking method based on two preference criteria: Chebyshev function and \(\in \)-indicator. In: IEEE CEC, pp. 2827–2834 (2015)

    Google Scholar 

  30. Chugh, T., Sindhya, K., Miettinen, K., Hakanen, J., Jin, Y.: On constraint handling in surrogate-assisted evolutionarymany-objective optimization. In: PPSN, pp. 214–224 (2016)

    Google Scholar 

  31. Parsons, M.G., Scott, R.L.: Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods. J. Ship Res. 48(1), 61–76 (2004)

    Google Scholar 

  32. Ray, T., Tai, K., Seow, C.: An evolutionary algorithm for multiobjective optimization. Eng. Opt. 33(3), 399–424 (2001)

    Article  Google Scholar 

  33. 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 TEVC 18(4), 577–601 (2014)

    Google Scholar 

  34. Li, K., Zhang, Q., Kwong, S., Li, M., Wang, R.: Stable matching-based selection in evolutionary multiobjective optimization. IEEE TEVC 18(6), 909–923 (2014)

    Google Scholar 

  35. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1994)

    MathSciNet  MATH  Google Scholar 

  36. 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)

    Article  MathSciNet  MATH  Google Scholar 

  37. Woldesenbet, Y.G., Yen, G.G., Tessema, B.G.: Constraint Handling in multiobjective evolutionary optimization. IEEE TEVC 13(3), 514–525 (2009)

    Google Scholar 

  38. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE TEVC 7(2), 117–132 (2003)

    Google Scholar 

  39. Krause, O., Glasmachers, T., Hansen, N., Igel, C.: Unbounded population MO-CMA-ES for the BI-objective BBOB test suite, pp. 1177–1184. Association for Computing Machinery (2016)

    Google Scholar 

  40. Tanabe, R., Ishibuchi, H., Oyama, A.: Benchmarking multi- and many-objective evolutionary algorithms under two optimization scenarios. IEEE Access 5, 19597–19619 (2017)

    Article  Google Scholar 

  41. Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by MEXT Development of Innovative Design and Production Processes that Lead the Way for the Manufacturing Industry in the Near Future through Priority Issue on Post-K computer.

Author information

Authors and Affiliations

  1. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, 252-5210, Japan

    Hiroaki Fukumoto & Akira Oyama

Authors
  1. Hiroaki Fukumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akira Oyama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroaki Fukumoto .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fukumoto, H., Oyama, A. (2018). A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77538-8_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation