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Self-adaptive Evolutionary Many-Objective Optimization Based on Relation \({\varepsilon }\)-Preferred

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Book cover Computational Intelligence (IJCCI 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 613))

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Abstract

Many real-world optimization problems consist of several mutually dependent subproblems. If more than three optimization objectives are involved in the optimization process, the so-called Many-Objective Optimization is a challenge in the area of multi-objective optimization. Often, the objectives have different levels of importance that have to be considered. For this, relation \({\varepsilon }\)-\(\textit{Preferred} \) has been presented, that enables to compare and rank multi-dimensional solutions. \({\varepsilon }\)-\(\textit{Preferred} \) is controlled by a parameter \({\varepsilon }\) that has influence on the quality of the results. In this paper for the setting of the epsilon values three heuristics have been investigated. To demonstrate the behavior and efficiency of these methods an Evolutionary Algorithm for the multi-dimensional Nurse Rostering Problem is proposed. It is shown by experiments that former approaches are outperformed by heuristics that are based on self-adaptive mechanisms.

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Notes

  1. 1.

    For the investigation of this approach the NRP has been used as application, because it consists of many objectives with different levels of priorities. There, in contrast to standard benchmarks for MOO (DTLZ [19]), the priorities are provided in the benchmark files.

  2. 2.

    Originally the benchmarks are designed for optimization using a Weighted Sum. Thus, the weights are justified by a planner and directly given in the benchmark.

  3. 3.

    The main reason for using NSGA-II for comparison is that it can easily be enlarged such that it can handle priorities as described in this paper. Other methods like e.g. Hype [4] are more suitable for Many-Objective Optimization, but it is not obvious how to incorporate the priorities. This is an interesting task for further developments.

References

  1. Fonseca, C., Fleming, P.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3(1), 1–16 (1995)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. Wiley, New York (2001)

    Google Scholar 

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

    Article  Google Scholar 

  5. Drechsler, N., Drechsler, R., Becker, B.: Multi-objective optimisation based on relation favour. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 154–166 (2001)

    Google Scholar 

  6. Hughes, E.: Radar waveform optimization as a many-objective application benchmark. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 700–714 (2007)

    Google Scholar 

  7. Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)

    Article  Google Scholar 

  8. Schmiedle, F., Drechsler, N., Große, D., Drechsler, R.: Priorities in multi-objective optimization for genetic programming. In: Genetic and Evolutionary Computation Conference, pp. 129–136 (2001)

    Google Scholar 

  9. Wickramasinghe, U., Li, X.: A distance metric for evolutionary many-objective optimization algorithms using user-preferences. In: 22nd Australasian Joint Conference on Advances in Artificial Intelligence (AI’09), pp. 443–453 (2009)

    Chapter  Google Scholar 

  10. Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Articulating user preferences in many-objective problems by sampling the weighted hypervolume. In: Genetic and Evolutionary Computation Conference, pp. 555–562 (2009)

    Google Scholar 

  11. 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 (2012)

    Article  Google Scholar 

  12. Drechsler, N., Sülflow, S., Drechsler, R.: Incorporating user preferences in many-objective optimization using relation \(\epsilon \)-preferred. In: International Conference on Evolutionary Computation Theory and Applications (2013)

    Google Scholar 

  13. Geiger, M.: Multi-criteria curriculum-based course timetabling—a comparison of a weighted sum and a reference point based approach. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 290–304 (2009)

    Google Scholar 

  14. Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Publisher Company, Inc, Reading (1989)

    Google Scholar 

  15. Corne, D., Knowles, J.: Techniques for highly multiobjective optimization: theorie and applications. In: Genetic and Evolutionary Computation Conference, pp. 773–780 (2007)

    Google Scholar 

  16. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: IEEE Congress on Evolutionary Computation, pp. 2424–2431 (2008)

    Google Scholar 

  17. Sülflow, A., Drechsler, N., Drechsler, R.: Robust multi-objective optimization in high-dimensional spaces. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 715–726 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  19. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)

    Google Scholar 

  20. Burke, E., Curtois, T., Qu, R., Vanden-Berghe, G.: Problem model for nurse rostering benchmark instances. Technical report, ASAP, School of Computer Science, University of Nottingham, UK (2012)

    Google Scholar 

  21. Cormen, T., Leierson, C., Rivest, R.: Introduction to Algorithms. MIT Press, Cambridge (1990)

    Google Scholar 

  22. Burke, E., Causmaecker, P.D., Berghe, G., Landeghem, H.: The state of the art of nurse rostering. J. Sched. 7, 441–499 (2004)

    Article  MathSciNet  Google Scholar 

  23. Benchmarks: Employee scheduling benchmark data set: Technical report, ASAP, School of Computer Science, The University of Nottingham, UK (2012). http://www.cs.nott.ac.uk/~tec/nrp/

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Acknowledgments

I’d like to thank André Sülflow and Rolf Drechsler for helpful discussions and comments and for their contributions to previous work.

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Authors and Affiliations

  1. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Nicole Drechsler

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  1. Nicole Drechsler
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Correspondence to Nicole Drechsler .

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Editors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  2. Departamento de Engenharia Informatica Polo II - Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    António Dourado

  3. aSEEB-ISR-IST Av. Rovisco Pais, 1, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  4. Rua do Vale de Chaves, Estefanilha, Polytechnic Institute of Setúbal / INSTICC, Setúbal, Portugal

    Joaquim Filipe

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Drechsler, N. (2016). Self-adaptive Evolutionary Many-Objective Optimization Based on Relation \({\varepsilon }\)-Preferred. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-23392-5_2

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