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Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms

  • Magdalena A. K. Lang
  • Christian GrimmeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)

Abstract

Evolutionary algorithms allow for solving a wide range of multi-objective optimization problems. Nevertheless for complex practical problems, including domain knowledge is imperative to achieve good results. In many domains, single-objective expert knowledge is available, but its integration into modern multi-objective evolutionary algorithms (MOEAs) is often not trivial and infeasible for practitioners. In addition to the need of modifying algorithm architectures, the challenge of combining single-objective knowledge to multi-objective rules arises. This contribution takes a step towards a multi-objective optimization framework with defined interfaces for expert knowledge integration. Therefore, multi-objective mutation and local search operators are integrated into the two MOEAs MOEA/D and R-NSGAII. Results from experiments on exemplary machine scheduling problems prove the potential of such a concept and motivate further research in this direction.

Keywords

Multi-objective evolutionary algorithm Expert knowledge integration MOEA/D R-NSGAII Scheduling 

References

  1. 1.
    Bagchi, T.P.: Multiobjective Scheduling by Genetic Algorithms. Springer, New York (1999)CrossRefzbMATHGoogle Scholar
  2. 2.
    Burke, E.K., Gendreau, M., Hyde, M.R., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. JORS 64(12), 1695–1724 (2013)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Deb, K., Sundar, J., Udaya Bhaskara Rao, N., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comput. Intell. Res. 2(3), 273–286 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Grimme, C., Kemmerling, M., Lepping, J.: On the integration of theoretical single-objective scheduling results for multi-objective problems. In: Tantar, E., Tantar, A.A., Bouvry, P., Del Moral, P., Legrand, P., Coello Coello, C., Schuetze, O. (eds.) EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation, Studies in Computational Intelligence, vol. 447, pp. 333–363. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Grimme, C., Lepping, J., Schwiegelshohn, U.: Multi-criteria scheduling: an agent-based approach for expert knowledge integration. J. Sched. 16(4), 369–383 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hoogeveen, H.: Multicriteria scheduling. Eur. J. Oper. Res. 167(3), 592–623 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Ishibuchi, H., Hitotsuyanagi, Y., Tsukamoto, N., Nojima, Y.: Use of heuristic local search for single-objective optimization in multiobjective memetic algorithms. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 743–752. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-87700-4_74 CrossRefGoogle Scholar
  9. 9.
    Ishibuchi, H., Hitotsuyanagi, Y., Tsukamoto, N., Nojima, Y.: Use of biased neighborhood structures in multiobjective memetic algorithms. Soft Comput. 13(8), 795–810 (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    Konstantinidis, A., Yang, K.: Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Appl. Soft Comput. 11(6), 4117–4134 (2011)CrossRefGoogle Scholar
  11. 11.
    Miettinen, K.: Nonlinear Multiobjective Optimization. International Series in Operations Research and Management Science, vol. 12. Springer, New York (1998)zbMATHGoogle Scholar
  12. 12.
    Nagar, A., Haddock, J., Heragu, S.: Multiple and bicriteria scheduling: a literature survey. Eur. J. Oper. Res. 81(1), 88–104 (1995)CrossRefzbMATHGoogle Scholar
  13. 13.
    Nebro, A.J., Durillo, J.J.: jMetal 4.5 user manual (21 Jan 2014)Google Scholar
  14. 14.
    Peng, W., Zhang, Q.: Network topology planning using MOEA/D with objective-guided operators. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7492, pp. 62–71. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32964-7_7 CrossRefGoogle Scholar
  15. 15.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 4th edn. Springer, New York (2012)CrossRefzbMATHGoogle Scholar
  16. 16.
    Silva, J.D.L., Burke, E.K., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In: Gandibleux, X., Sevaux, M., Soerensen, K., T’kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation - Part I, Lecture Notes in Economics and Mathematical Systems, vol. 535, pp. 91–129. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Smith, W.E.: Various optimizers for single-stage production. Nav. Res. Logistics Q. 3(1), 59–66 (1956)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wang, J., Cai, Y.: Multiobjective evolutionary algorithm for frequency assignment problem in satellite communications. Soft Comput. 19(5), 1229–1253 (2015)CrossRefGoogle Scholar
  19. 19.
    Wassenhove, L.N.V., Gelders, F.: Solving a bicriterion scheduling problem. Eur. J. Oper. Res. 4(1), 42–48 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Westfälische Wilhelms-Universität MünsterMünsterGermany

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