Skip to main content
SpringerLink
Log in

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

  • Chapter
Advances in Multi-Objective Nature Inspired Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 272))

Summary

This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://cs.gmu.edu/~eclab/projects/ecj/

  2. http://home.gna.org/momh/

  3. http://shark-project.sourceforge.net/

  4. OMG unified modeling language specification. Object Management Group (2000)

    Google Scholar 

  5. Basseur, M., Seynhaeve, F., Talbi, E.G.: Design of multi-objective evolutionary algorithms: Application to the flow-shop scheduling problem. In: Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawai, USA, vol. 2, pp. 1151–1156 (2002)

    Google Scholar 

  6. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653–1669 (2007)

    Article  MATH  Google Scholar 

  7. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA — a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Boisson, J.C., Jourdan, L., Talbi, E.G.: ParadisEO-MO. Tech. rep. (2008)

    Google Scholar 

  9. Boisson, J.C., Jourdan, L., Talbi, E.G., Horvath, D.: Parallel multi-objective algorithms for the molecular docking problem. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008), Sun Valley Resort, Idaho, USA (2008)

    Google Scholar 

  10. Cahon, S., Melab, N., Talbi, E.G.: ParadisEO: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)

    Article  Google Scholar 

  11. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)

    MATH  Google Scholar 

  12. Corne, D., Knowles, J.D., Oates, M.J.: The pareto envelope-based selection algorithm for multi-objective optimisation. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  13. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  15. Deb, K., Mohan, M., Mishra, S.: Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evolutionary Computation 13(4), 501–525 (2005)

    Article  Google Scholar 

  16. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., Jain, R., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, ch. 6, pp. 105–145. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Durillo, J.J., Nebro, A.J., Luna, F., Dorrosoro, B., Alba, E.: jMetal: A java framework for developing multi-objective optimization metaheuristics. Tech. Rep. ITI-2006-10, University of Málaga (2006)

    Google Scholar 

  18. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 416–423. Morgan Kaufmann, Urbana-Champaign (1993)

    Google Scholar 

  19. Fourman, M.P.: Compaction of symbolic layout using genetic algorithms. In: Grefensette, J.J. (ed.) Proceedings of the 1st International Conference on Genetic Algorithms (ICGA 1985), pp. 141–153. Lawrence Erlbaum Associates, Pittsburgh (1985)

    Google Scholar 

  20. Gagné, C., Parizeau, M.: Genericity in evolutionary computation software tools: Principles and case study. International Journal on Artificial Intelligence Tools 15(2), 173–194 (2006)

    Article  Google Scholar 

  21. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  22. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Second International Conference on Genetic Algorithms and their application, pp. 41–49. Lawrence Erlbaum Associates, Inc., Mahwah (1987)

    Google Scholar 

  23. Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics 5, 287–326 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  24. Helbig, S., Pateva, D.: On several concepts for ε-efficiency. OR Spektrum 16(3), 179–186 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  25. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Abor (1975)

    Google Scholar 

  26. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: IEEE Congress on Evolutionary Computation (CEC 1994), pp. 82–87. IEEE Press, Piscataway (1994)

    Google Scholar 

  27. Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man and Cybernetics 28, 392–403 (1998)

    Article  Google Scholar 

  28. Jong, K.A.D.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D thesis, Ann Arbor, University of Michigan (1975)

    Google Scholar 

  29. Jourdan, L., Khabzaoui, M., Dhaenens, C., Talbi, E.G.: A hybrid evolutionary algorithm for knowledge discovery in microarray experiments. In: Olariu, S., Zomaya, A.Y. (eds.) Handbook of Bioinspired Algorithms and Applications, ch. 28, pp. 489–505. CRC Press, Boca Raton (2005)

    Google Scholar 

  30. Keijzer, M., Merelo, J.J., Romero, G., Schoenauer, M.: Evolving objects: A general purpose evolutionary computation library. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 231–244. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  31. Landa Silva, J.D., Burke, E., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In: Gandibleux, X., Sevaux, M., Sörensen, K., T’kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. LNEMS, vol. 535, pp. 91–129. Springer, Berlin (2004)

    Google Scholar 

  32. Liefooghe, A., Basseur, M., Jourdan, L., Talbi, E.G.: Combinatorial optimization of stochastic multi-objective problems: an application to the flow-shop scheduling problem. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 457–471. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  33. Liefooghe, A., Basseur, M., Jourdan, L., Talbi, E.G.: ParadisEO-MOEO: A framework for evolutionary multi-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 386–400. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  34. Liefooghe, A., Jourdan, L., Talbi, E.G.: Metaheuristics and their hybridization to solve the bi-objective ring star problem: a comparative study. Tech. Rep. RR-6515, Institut National de Recherche en Informatique et Automatique, INRIA (2008)

    Google Scholar 

  35. Meunier, H., Talbi, E.G., Reininger, P.: A multiobjective genetic algorithm for radio network optimization. In: IEEE Congress on Evolutionary Computation (CEC 2000), pp. 317–324. IEEE Press, San Diego (2000)

    Google Scholar 

  36. Miettinen, K.: Nonlinear Multiobjective Optimization. International Series in Operations Research and Management Science, vol. 12. Kluwer Academic Publishers, Boston (1999)

    MATH  Google Scholar 

  37. Molina, J., Santana, L.V., Hernández-Díaz, A.G., Coello Coello, C.A., Caballero, R.: g-dominance: Reference point based dominance for multiobjective metaheuristics. European Journal of Operational Research 197(2), 685–692 (2009)

    Article  MATH  Google Scholar 

  38. Poles, S., Vassileva, M., Sasaki, D.: Multiobjective optimization software. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 329–348. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  39. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefensette, J.J. (ed.) Proceedings of the 1st International Conference on Genetic Algorithms (ICGA 1985), pp. 93–100. Lawrence Erlbaum Associates, Pittsburgh (1985)

    Google Scholar 

  40. Schuetze, O., Jourdan, L., Legrand, T., Talbi, E.G., Wojkiewicz, J.L.: New analysis of the optimization of electromagnetic shielding properties using conducting polymers and a multi-objective approach. Polymers for Advanced Technologies 19(7), 762–769 (2008)

    Article  Google Scholar 

  41. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)

    Article  Google Scholar 

  42. Streichert, F., Ulmer, H.: JavaEvA: a java based framework for evolutionary algorithms. Tech. Rep. WSI-2005-06, Centre for Bioinformatics Tübingen (ZBIT) of the Eberhard-Karls-University, Tübingen (2005)

    Google Scholar 

  43. Talbi, E.G., Cahon, S., Melab, N.: Designing cellular networks using a parallel hybrid metaheuristic on the computational grid. Computer Communications 30(4), 698–713 (2007)

    Article  Google Scholar 

  44. Talbi, E.G., Jourdan, L., Garcia-Nieto, J., Alba, E.: Comparison of population based metaheuristics for feature selection: Application to microarray data classification. In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 2008), pp. 45–52. IEEE, Los Alamitos (2008)

    Chapter  Google Scholar 

  45. Tan, K.C., Lee, T.H., Khoo, D., Khor, E.F.: A multi-objective evolutionary algorithm toolbox for computer-aided multi-objective optimization. IEEE Transactions on Systems, Man and Cybernetics: Part B (Cybernetics) 31(4), 537–556 (2001)

    Article  Google Scholar 

  46. T’Kindt, V., Billaut, J.C.: Multicriteria Scheduling: Theory, Models and Algorithms. Springer, Berlin (2002)

    MATH  Google Scholar 

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

    Google Scholar 

  48. 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)

    Google Scholar 

  49. Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on evolutionary multiobjective optimization. In: Gandibleux, X., Sevaux, M., Swrensen, K. (eds.) Metaheuristics for Multiobjective Optimisation. LNEMS, vol. 535, pp. 3–38. Springer, Heidelberg (2004)

    Google Scholar 

  50. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Tech. Rep. 103, Computer Engineering and Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)

    Google Scholar 

  51. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

  52. Zitzler, E., Thiele, L., Laumanns, M., Foneseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire d’Informatique Fondamentale de Lille (LIFL), UMR CNRS 8022, Université Lille 1, Bâtiment M3, 59655, Villeneuve d’Ascq cedex, France

    Arnaud Liefooghe, Laetitia Jourdan & El-Ghazali Talbi

  2. Parc Scientifique de la Haute Borne, INRIA Lille-Nord Europe, 40 avenue Halley, 59650, Villeneuve d’Ascq, France

    Arnaud Liefooghe, Laetitia Jourdan, Thomas Legrand, Jérémie Humeau & El-Ghazali Talbi

Authors
  1. Arnaud Liefooghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laetitia Jourdan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Legrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jérémie Humeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. El-Ghazali Talbi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Depto. de Computación, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, 07300, México, D.F., Mexico

    Carlos A. Coello Coello

  2. Parc scientifique de la Haute-Borne, LIFL/CNRS/Polytech’Lille/INRIA, Bâtiment A, Park Plaza, 40,Avenue Halley, 59650, Villeneuve d’Ascq, France

    Clarisse Dhaenens

  3. Parc scientifique de la Haute-Borne, INRIA/LIFL/CNRS, Bâtiment A, Park Plaza, 40, Avenue Halley, 59650, Villeneuve d’Ascq, France

    Laetitia Jourdan

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Liefooghe, A., Jourdan, L., Legrand, T., Humeau, J., Talbi, EG. (2010). ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization. In: Coello Coello, C.A., Dhaenens, C., Jourdan, L. (eds) Advances in Multi-Objective Nature Inspired Computing. Studies in Computational Intelligence, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11218-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11218-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11217-1

  • Online ISBN: 978-3-642-11218-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation