Skip to main content
SpringerLink
Log in

Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms

  • Conference paper
  • First Online:
Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

Included in the following conference series:

Abstract

Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.

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

Notes

  1. 1.

    Strictly speaking, not all of these algorithms are evolutionary algorithms, we consider to change MOEA to MOMH (Multiobjective MetaHeuristic) in the future version.

References

  1. Protege webpage (2016). http://protege.stanford.edu/

  2. Allmendinger, R., Li, X., Branke, J.: Reference point-based particle swarm optimization using a steady-state approach. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 200–209. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89694-4_21

    Chapter  Google Scholar 

  3. Battiti, R., Passerini, A.: Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker. IEEE Trans. Evol. Comput. 14(5), 671–687 (2010)

    Article  Google Scholar 

  4. Bechikh, S., Kessentini, M., Said, L.B., Ghédira, K.: Chapter four-preference incorporation in evolutionary multiobjective optimization: a survey of the state-of-the-art. Adv. Comput. 98, 141–207 (2015)

    Article  Google Scholar 

  5. Bechikh, S., Said, L.B., Ghédira, K.: Searching for knee regions of the Pareto front using mobile reference points. Soft Comput. 15(9), 1807–1823 (2011)

    Article  Google Scholar 

  6. Ben Said, L., Bechikh, S., Ghédira, K.: The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans. Evol. Comput. 14(5), 801–818 (2010)

    Article  Google Scholar 

  7. Branke, J.: MCDA and multiobjective evolutionary algorithms. In: Greco, S., Ehrgott, M., Figueira, J.R. (eds.) Multiple Criteria Decision Analysis, vol. 233, pp. 977–1008. Springer, New York (2016)

    Chapter  Google Scholar 

  8. Branke, J., Corrente, S., Greco, S., Słowiński, R., Zielniewicz, P.: Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. Eur. J. Oper. Res. 250(3), 884–901 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  9. Branke, J., Greco, S., Slowinski, R., Zielniewicz, P.: Learning value functions in interactive evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 19(1), 88–102 (2015)

    Article  MATH  Google Scholar 

  10. Branke, J., Kaußler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Adv. Eng. Softw. 32(6), 499–507 (2001)

    Article  MATH  Google Scholar 

  11. Brockhoff, D., Bader, J., Thiele, L., Zitzler, E.: Directed multiobjective optimization based on the weighted hypervolume indicator. J. Multi-Criteria Dec. Anal. 20(5–6), 291–317 (2013)

    Article  Google Scholar 

  12. Coello, C.A.C.: Handling preferences in evolutionary multiobjective optimization: a survey. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 30–37. IEEE (2000)

    Google Scholar 

  13. Coello, C.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, New York (2007)

    MATH  Google Scholar 

  14. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, New York (2001)

    MATH  Google Scholar 

  15. Deb, K., Kumar, A.: Light beam search based multi-objective optimization using evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 2125–2132. IEEE (2007)

    Google Scholar 

  16. Deb, K., Kumar, A.: Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 781–788. ACM (2007)

    Google Scholar 

  17. Deb, K., Sinha, A., Korhonen, P.J., Wallenius, J.: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Trans. Evol. Comput. 14(5), 723–739 (2010)

    Article  Google Scholar 

  18. Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 635–642. ACM (2006)

    Google Scholar 

  19. Emmerich, M., Deutz, A., Kruisselbrink, J., Shukla, P.K.: Cone-based hypervolume indicators: construction, properties, and efficient computation. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 111–127. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37140-0_12

    Chapter  Google Scholar 

  20. Fernandez, E., Lopez, E., Lopez, F., Coello, C.A.C.: Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: the extended NOSGA method. Inf. Sci. 181(1), 44–56 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  21. Friedrich, T., Kroeger, T., Neumann, F.: Weighted preferences in evolutionary multi-objective optimization. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS (LNAI), vol. 7106, pp. 291–300. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25832-9_30

    Chapter  Google Scholar 

  22. Greco, S., Matarazzo, B., Slowinski, R.: Interactive evolutionary multiobjective optimization using dominance-based rough set approach. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  23. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  24. Horridge, M., Drummond, N., Goodwin, J., Rector, A.L., Stevens, R., Wang, H.: The Manchester OWL syntax. In: OWLed, vol. 216 (2006)

    Google Scholar 

  25. Horridge, M., Jupp, S., Moulton, G., Rector, A., Stevens, R., Wroe, C.: A practical guide to building OWL ontologies using Protégé 4 and CO-ODE tools edition 1.2. The University of Manchester (2009)

    Google Scholar 

  26. Jaimes, A.L., Montano, A.A., Coello, C.A.C.: Preference incorporation to solve many-objective airfoil design problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1605–1612. IEEE (2011)

    Google Scholar 

  27. Jaszkiewicz, A., Słowiński, R.: The light beam search approach-an overview of methodology applications. Eur. J. Oper. Res. 113(2), 300–314 (1999)

    Article  MATH  Google Scholar 

  28. Jin, Y., Sendhoff, B.: Incorporation of fuzzy preferences into evolutionary multiobjective optimization. In: GECCO, p. 683 (2002)

    Google Scholar 

  29. Kaur, G., Chaudhary, D.: Evolutionary computation ontology: E-learning system. In: 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), pp. 1–6. IEEE (2015)

    Google Scholar 

  30. Kim, J.H., Han, J.H., Kim, Y.H., Choi, S.H., Kim, E.S.: Preference-based solution selection algorithm for evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 16(1), 20–34 (2012)

    Article  Google Scholar 

  31. Korhonen, P.J., Laakso, J.: A visual interactive method for solving the multiple criteria problem. Eur. J. Oper. Res. 24(2), 277–287 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  32. Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., Emmerich, M.: An ontology of preference-based multiobjective evolutionary algorithms. arXiv:1609.08082, submitted to IEEE Transactions on Evolutionary Computation

  33. McGuinness, D.L., Van Harmelen, F., et al.: Owl web ontology language overview. W3C Recomm. 10(10), 77–213 (2004)

    Google Scholar 

  34. Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer, New York (2012)

    MATH  Google Scholar 

  35. Mohammadi, A., Omidvar, M.N., Li, X.: A new performance metric for user-preference based multi-objective evolutionary algorithms. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2825–2832. IEEE (2013)

    Google Scholar 

  36. Molina, J., Santana, L.V., Hernández-Díaz, A.G., Coello, C.A.C., Caballero, R.: g-dominance: reference point based dominance for multiobjective metaheuristics. Eur. J. Oper. Res. 197(2), 685–692 (2009)

    Article  MATH  Google Scholar 

  37. Mostaghim, S., Trautmann, H., Mersmann, O.: Preference-based multi-objective particle swarm optimization using desirabilities. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 101–110. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15871-1_11

    Google Scholar 

  38. Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: a guide to creating your first ontology (2001)

    Google Scholar 

  39. Ojalehto, V., Podkopaev, D., Miettinen, K.: Towards automatic testing of reference point based interactive methods. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 483–492. Springer, Cham (2016). doi:10.1007/978-3-319-45823-6_45

    Chapter  Google Scholar 

  40. Pedro, L.R., Takahashi, R.H.C.: Decision-maker preference modeling in interactive multiobjective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 811–824. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37140-0_60

    Chapter  Google Scholar 

  41. Rachmawati, L., Srinivasan, D.: Preference incorporation in multi-objective evolutionary algorithms: a survey. In: IEEE Congress on Evolutionary Computation, 2006. CEC 2006, pp. 962–968. IEEE (2006)

    Google Scholar 

  42. Rachmawati, L., Srinivasan, D.: Incorporating the notion of relative importance of objectives in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 14(4), 530–546 (2010)

    Article  Google Scholar 

  43. Shukla, P.K., Emmerich, M., Deutz, A.: A theoretical analysis of curvature based preference models. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 367–382. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37140-0_29

    Chapter  Google Scholar 

  44. Shukla, P.K., Hirsch, C., Schmeck, H.: A framework for incorporating trade-off information using multi-objective evolutionary algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 131–140. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15871-1_14

    Google Scholar 

  45. Sinha, A., Korhonen, P., Wallenius, J., Deb, K.: An Interactive Evolutionary Multi-objective Optimization Method Based on Polyhedral Cones. In: Blum, C., Battiti, R. (eds.) LION 2010. LNCS, vol. 6073, pp. 318–332. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13800-3_33

    Chapter  Google Scholar 

  46. Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  47. Thiele, L., Miettinen, K., Korhonen, P.J., Molina, J.: A preference-based evolutionary algorithm for multi-objective optimization. Evol. Comput. 17(3), 411–436 (2009)

    Article  Google Scholar 

  48. Trautmann, H., Mehnen, J.: Preference-based Pareto optimization in certain and noisy environments. Eng. Optim. 41(1), 23–38 (2009)

    Article  MathSciNet  Google Scholar 

  49. Trautmann, H., Wagner, T., Brockhoff, D.: R2-EMOA: focused multiobjective search using R2-indicator-based selection. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS, vol. 7997, pp. 70–74. Springer, Heidelberg (2013). doi:10.1007/978-3-642-44973-4_8

    Chapter  Google Scholar 

  50. Vira, C., Haimes, Y.Y.: Multiobjective decision making: theory and methodology. No. 8, North-Holland (1983)

    Google Scholar 

  51. Wagner, T., Trautmann, H.: Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functions. IEEE Trans. Evol. Comput. 14(5), 688–701 (2010)

    Article  Google Scholar 

  52. Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Application. LNE, vol. 177, pp. 468–486. Springer, Heidelberg (1980)

    Chapter  Google Scholar 

  53. Yevseyeva, I., Guerreiro, A.P., Emmerich, M.T.M., Fonseca, C.M.: A portfolio optimization approach to selection in multiobjective evolutionary algorithms. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 672–681. Springer, Cham (2014). doi:10.1007/978-3-319-10762-2_66

    Google Scholar 

Download references

Acknowledgement

Longmei Li acknowledges financial support from China Scholarship Council (CSC). Heike Trautmann and Michael Emmerich acknowledge support by the European Research Center for Information Systems (ERCIS).

Author information

Authors and Affiliations

  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China

    Longmei Li & Ning Jing

  2. Leiden Institute of Advanced Computer Science, Leiden University, 2333CA, Leiden, The Netherlands

    Longmei Li & Michael Emmerich

  3. Faculty of Technology, School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK

    Iryna Yevseyeva

  4. Instituto Universitário de Lisboa (ISCTE-IUL), University Institute of Lisbon, ISTAR-IUL, Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Vitor Basto-Fernandes

  5. School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic Institute of Leiria, 2411-901, Leiria, Portugal

    Vitor Basto-Fernandes

  6. Department of Information Systems, University of Münster, 48149, Münster, Germany

    Heike Trautmann

Authors
  1. Longmei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iryna Yevseyeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vitor Basto-Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heike Trautmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ning Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Emmerich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Longmei Li .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., Emmerich, M. (2017). Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54157-0_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation