Skip to main content
SpringerLink
Log in

The Influence of Correlated Objectives on Different Types of P-ACO Algorithms

  • Conference paper
Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8600))

Abstract

The influence of correlated objectives on different types of P-ACO algorithms for solutions of multi objective optimization problems is investigated. Therefore, a simple method to create multi objective optimization problems with correlated objectives is proposed. Theoretical results show how certain correlations between the objectives can be obtained. The method is applied to the Traveling Salesperson problem. The influence of the correlation type and strength on the optimization behavior of different P-ACO algorithms is analyzed empirically. A particular focus is given on P-ACOs with ranking methods.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Xu, Y., Qu, R., Li, R.: A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems. Ann. Oper. Res. 206(1), 527–555 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  2. Jaszkiewicz, A.: Genetic local search for multi-objective combinatorial optimization. European Journal of Operational Research 137(1), 50–71 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  3. Knowles, J.D., Corne, D.: Towards landscape analyses to inform the design of hybrid local search for the multiobjective quadratic assignment problem. HIS 87, 271–279 (2002)

    Google Scholar 

  4. Paquete, L., Stützle, T.: A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices. Eur. J. Oper. Res. 169(3), 943–959 (2006)

    Article  MATH  Google Scholar 

  5. López-Ibánez, M., Paquete, L., Stützle, T.: Hybrid population-based algorithms for the bi-objective quadratic assignment problem. Journal of Mathematical Modelling and Algorithms 5(1), 111–137 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  6. López-Ibáñez, M., Paquete, L., Stützle, T.: On the design of ACO for the biobjective quadratic assignment problem. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 214–225. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Garrett, D., Dasgupta, D., Vannucci, J., Simien, J.: Applying hybrid multiobjective evolutionary algorithms to the sailor assignment problem. In: Jain, L.C., Palade, V., Srinivasan, D. (eds.) Advances in Evolutionary Computing for System Design. SCI, vol. 66, pp. 269–301. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: Analyzing the effect of objective correlation on the efficient set of MNK-landscapes. In: Coello Coello, C.A. (ed.) LION 5. LNCS, vol. 6683, pp. 116–130. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: Pareto local optima of multiobjective NK-landscapes with correlated objectives. In: Merz, P., Hao, J.K. (eds.) EvoCOP 2011. LNCS, vol. 6622, pp. 226–237. Springer, Heidelberg (2011)

    Google Scholar 

  10. Shi, C., Yu, P., Yan, Z., Huang, Y., Wang, B.: Comparison and selection of objective functions in multiobjective community detection. Computational Intelligence (to appear, 2013)

    Google Scholar 

  11. Ishibuchi, H., Akedo, N., Ohyanagi, H., Nojima, Y.: Behavior of EMO algorithms on many-objective optimization problems with correlated objectives. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1465–1472. IEEE (2011)

    Google Scholar 

  12. Ishibuchi, H., Akedo, N., Nojima, Y.: A study on the specification of a scalarizing function in MOEA/D for many-objective knapsack problems. In: Nicosia, G., Pardalos, P. (eds.) LION 7. LNCS, vol. 7997, pp. 231–246. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Ishibuchi, H., Yamane, M., Nojima, Y.: Effects of duplicated objectives in many-objective optimization problems on the search behavior of hypervolume-based evolutionary algorithms. In: 2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), pp. 25–32. IEEE (2013)

    Google Scholar 

  14. Brockhoff, D., Saxena, D., Deb, K., Zitzler, E.: On handling a large number of objectives a posteriori and during optimization. Natural Computing Series, pp. 377–403. Springer (2008)

    Google Scholar 

  15. Goel, T., Vaidyanathan, R., Haftka, R.T., Shyy, W., Queipo, N.V., Tucker, K.: Response surface approximation of pareto optimal front in multi-objective optimization. Comput. Method. Appl. M. 196(4), 879–893 (2007)

    Article  MATH  Google Scholar 

  16. Murata, T., Taki, A.: Examination of the performance of objective reduction using correlation-based weighted-sum for many objective knapsack problems. In: 10th International Conference on Hybrid Intelligent Systems (HIS), pp. 175–180. IEEE (2010)

    Google Scholar 

  17. Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoWorkshops 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Knowles, J., Corne, D.: Instance generators and test suites for the multiobjective quadratic assignment problem. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 295–310. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  19. Liefooghe, A., Paquete, L., Simões, M., Figueira, J.R.: Connectedness and local search for bicriteria knapsack problems. In: Merz, P., Hao, J.K. (eds.) EvoCOP 2011. LNCS, vol. 6622, pp. 48–59. Springer, Heidelberg (2011)

    Google Scholar 

  20. Corne, D., Knowles, J.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 773–780. ACM (2007)

    Google Scholar 

  21. Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., Nojima, Y.: Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 649–656. ACM (2008)

    Google Scholar 

  22. Lopez Jaimes, A., Santa Quintero, L., Coello Coello, C.A.: Study of preference relations in many-objective optimization. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 611–618. ACM (2009)

    Google Scholar 

  23. Lopez Jaimes, A., Coello Coello, C.A.: Study of preference relations in many-objective optimization. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 611–618. ACM (2009)

    Google Scholar 

  24. Moritz, R., Reich, E., Schwarz, M., Bernt, M., Middendorf, M.: Refined ranking relations for multi objective optimization and application to P-ACO. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, pp. 65–72. ACM (2013)

    Google Scholar 

  25. Drechsler, N., Drechsler, R., Becker, B.: Multi-objective optimisation based on relation favour. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 154–166. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  26. Garza-Fabre, M., Toscano Pulido, G., Coello Coello, C.A.: Ranking methods for many-objective optimization. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds.) MICAI 2009. LNCS, vol. 5845, pp. 633–645. Springer, Heidelberg (2009)

    Google Scholar 

  27. Garza Fabre, M., Toscano Pulido, G., Coello Coello, C.A.: Alternative fitness assignment methods for many-objective optimization problems. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) EA 2009. LNCS, vol. 5975, pp. 146–157. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  28. Angus, D.: Crowding population-based ant colony optimisation for the multi-objective travelling salesman problem. In: IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp. 333–340. IEEE (2007)

    Google Scholar 

  29. Guntsch, M., Middendorf, M.: Solving multi-criteria optimization problems with population-based ACO. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 464–478. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  30. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Parallel Computing and Complex Systems Group, Institute of Computer Science, University of Leipzig, Germany

    Ruby L. V. Moritz, Enrico Reich, Matthias Bernt & Martin Middendorf

Authors
  1. Ruby L. V. Moritz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrico Reich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias Bernt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Middendorf
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, IKERBASQUE, Basque Foundation for Science, University of the Basque Country, Paseo Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Christian Blum

  2. School of Natural Sciences, Department of Computing Science and Mathematics, University of Stirling, Room 4B104, Cottrell Building, FK9 4LA, Stirling, UK

    Gabriela Ochoa

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moritz, R.L.V., Reich, E., Bernt, M., Middendorf, M. (2014). The Influence of Correlated Objectives on Different Types of P-ACO Algorithms. In: Blum, C., Ochoa, G. (eds) Evolutionary Computation in Combinatorial Optimisation. EvoCOP 2014. Lecture Notes in Computer Science, vol 8600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44320-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44320-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44319-4

  • Online ISBN: 978-3-662-44320-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation