Skip to main content
SpringerLink
Log in

On the Computational Effectiveness of Multiple Objective Metaheuristics

  • Conference paper
Multiple Objective and Goal Programming

Part of the book series: Advances in Soft Computing ((AINSC,volume 12))

Abstract

The paper describes a technique for comparison of computational effectiveness of two approaches to generation of approximately Pareto-optimal solutions with the use of metaheuristics. In the on-line generation approach the approximately Pareto-optimal solutions are generated during the interactive process, e.g. by optimization of some scalarizing functions. In the off-line generation approach, the solutions are generated prior to the interactive process with the use of multiple objective metaheuristics. The results of experiment on travelling salesperson instances indicate that in the case of some multiple objective metheuristics the off-line generation approach may be computationally effective alternative to the on-line generation of approximately Pareto-optimal solutions.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Borges P.C., Hansen P.H. (1998), A basis for future successes in multiobjective combinatorial optimization. Technical Report, Department of Mathematical Modelling, Technical University of Denmark,1MM-REP-1998–8.

    Google Scholar 

  2. Czyzak P., Jaszkiewicz A. (1998), Pareto simulated annealing–a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7, 34–47.

    Article  Google Scholar 

  3. Finkel R.A. and Bentley J.L. (1974), Quad Trees, A data structure for retrieval on composite keys. Acta Informatica, 4, 1–9.

    Article  Google Scholar 

  4. Fonseca C.M., Fleming P.J. (1993), Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest (Ed.), Genetic Algorithms: Proceedings of 5 31 International Conference, San Mateo, CA, Morgan Kaufmann, 416–423.

    Google Scholar 

  5. Freisleben B., Merz P. (1996), A genetic local search algorithm for travelling salesman problem. In H.-M. Voigt, W. Ebeling, I. Rechenberg, II.-P. Schwefel (eds.), Proceedings of the 4` h Conference on Parallel Problem Solving fram Nature- PPSN IV, 890–900.

    Chapter  Google Scholar 

  6. Gandibleux, X., Mezdaoui N., Fréville A. (1996). A tabu search procedure to solve multiobjective combinatorial optimization problems, In R. Caballero, R. Steuer (Eds.), Proceedings volume ofMOPGP `96,, Springer-Verlag.

    Google Scholar 

  7. Habenicht W. (1982), Quad Trees, A datastructure for discrete vector optimization problems Lecture Notes in Economics and Mathematical Systems, 209, 136–145.

    Article  Google Scholar 

  8. Hansen M. (1997), Tabu search for multiobjective optimization: MOTS, presented at the 13th MCDM conference, Cape Town, South Africa, January 6–10.

    Google Scholar 

  9. Horn. J., Nafpliotis N. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, IEEE, New York, 82–87.

    Chapter  Google Scholar 

  10. Hwang C.-L. and Masud A.S.M. (1979). Mutiple Objective Decision Making - Methods and Applications, Springer, Berlin.

    Book  Google Scholar 

  11. Hwang C.-L., Paidy S.R., Yoon K. and Masud A.S.M. (1980), Mathematical programming with multiple objectives: A tutorial. Comput. Oper. Res., 7, 5–31.

    Article  Google Scholar 

  12. Jaszkiewicz A. (1998). Genetic local search for multiple objective combinatorial optimization. Research report, Institute of Computing Science, Poznan University of Technology, RA-014/98, pp. 23.

    Google Scholar 

  13. Jaszkiewicz A., Slowinski R. (1997). The LBS-Discrete Interactive Procedure for Multiple-Criteria Analysis of Decision Problems. In: J. Climaco (red.) Multicriteria Analysis. Proceedings of the XIth International Conference on MCDM, 1–6, August 1994, Coimbra, Portugal, Springer-Verlag, Berlin–Heidelberg, 320–330.

    Google Scholar 

  14. Köksalan M., Karwan M.H. and Zionts S. (1988). An Approach for Solving Discrete Alternative Multiple Criteria Problems Involving Ordinal Criteria. Naval Research Logistics, 35, 6, 625–642.

    Article  Google Scholar 

  15. Korhonen P. (1988). A Visual Reference Direction Approach to Solving Discrete Multiple Criteria Problems. EJOR, 34, 2, 152–159.

    Article  Google Scholar 

  16. Korhonen P. Wallenius J. and Zionts S. (1984). Solving the Discrte Multiple Criteria Problem Using Convex Cones. Management Science, 30, 11, 1336–1345.

    Article  Google Scholar 

  17. Lotfi V., Stewart T.J. and Zionts S. (1992). An aspiration-level interactive model for multiple criteria decision making. Comput. Ops. Res., 19, 677–681.

    Article  Google Scholar 

  18. Malakooti B. (1989). Theories and an Exact Interactive Paired-COmparison Approach for Discrete Multiple Criteria Problems IEEE Transactions on Systems, Man, and Cybernetics, 19, 2, 365–378.

    Article  Google Scholar 

  19. Merz P., Freisleben B., Genetic Local Search for the TSP: New Results, hi Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE Press, 159–164, 1997.

    Google Scholar 

  20. Reinelt G. (1991). TSPLIB — a traveling salesman problem library. ORSA Journal of Computing, 3, 4, 376–384.

    Article  Google Scholar 

  21. Schaffer J.D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In: J.J. Grefenstette (ed.), Genetic Algorithms and Their Applications: Proceedings of the Third International Conference on Genetic Algorithms, Lawrence Erlbaum, Hillsdale, NJ, 93–100.

    Google Scholar 

  22. Serafmi P (1994). Simulated annealing for multiple objective optimization problems. In: Tzeng G.H., Wang H.F., Wen V.P., Yu P.L. (eds), Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, Springer Verlag, 283–292.

    Google Scholar 

  23. Shin W.S. and Ravindran A. (1991). Interactive multiple objective optimization: survey I–continuous case, Comput. Oper. Res., 18, 97–114.

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Steuer R.E. (1986). Multiple Criteria Optimization - Theory, Computation and Application, Wiley, New York.

    Google Scholar 

  26. Sun M., Steuer R. E. (1996). htterQuad: An Interactive Quad Tree Based Procedure for Solving the Discrete Alternative Multiple Criteria Problem. European Journal of Operational Research, 89, No. 3, 462–472.

    Google Scholar 

  27. Taner O.V. and Köksalan M.M. (1991). Experiments and an Improved Method for Solving the Discrete Alternative Multiple-Criteria Problem. Journal of the Operational Research Society, 42, 5, 383–392.

    Google Scholar 

  28. Ulungu E.L. and Teghem J. (1994). Multiobjective Combinatorial Optimization Problems: A Survey. Journal of Multi-Criteria Decision Analysis, 3, 83–101.

    Article  Google Scholar 

  29. Ulungu E.L., Teghem J., Fortemps Ph., Tuyttens (1999). MOSA method: a toll for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8, 221–236.

    Article  Google Scholar 

  30. Wierzbicki A.P. (1980), The use of reference objective in Multiobjective Optimization. In: Fandel G. and Gal T. (eds.) Multiple Criteria Decision Making, Theory and Application, Springer-Verlag, Berlin, 468–486.

    Google Scholar 

  31. Wierzbicki A.P. (1986), On the completeness and constructiveness of parametric characterization to vector optimization problems. OR Spektrum, 8, 73–87.

    Article  Google Scholar 

  32. Zionts S. (1981). A Multiple Criteria Method for Choosing among Discrete Alternatives. EJOR, 7, 1, 143–147

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 3a, 60-965, Poznań, Poland

    Andrzej Jaszkiewicz

Authors
  1. Andrzej Jaszkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Operations Research Department, The Karol Adamiecki University of Economics in Katowice, ul. Bogucicka 14, 40-226, Katowice, Poland

    Tadeusz Trzaskalik  & Jerzy Michnik  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jaszkiewicz, A. (2002). On the Computational Effectiveness of Multiple Objective Metaheuristics. In: Trzaskalik, T., Michnik, J. (eds) Multiple Objective and Goal Programming. Advances in Soft Computing, vol 12. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1812-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1812-3_7

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1409-5

  • Online ISBN: 978-3-7908-1812-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation