Skip to main content

A Property Preserving Method for Extending a Single-Objective Problem Instance to Multiple Objectives with Specific Correlations

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9595))

Included in the following conference series:

Abstract

A method is proposed to generate multi-objective optimization problem instances from a corresponding single-objective instance. The user of the method can specify the correlations between the generated the objectives. Different from existing instance generation methods the new method allows to keep certain properties of the original single-objective instance. In particular, we consider optimization problems where the objective is defined by a matrix, e.g., a distance matrix for the Traveling Salesperson problem (TSP) or a flow matrix for the Quadratic Assignment problem. It is shown that the method creates new distance matrices with specific correlations between each other and also have the same average distance and variance of distances as the distance matrix of the original instance. This property is important, e.g., when the influence of correlations between the objectives on the behavior of metaheuristics for the multi-objective TSP are investigated. Some properties of the new method are shown theoretically. In an empirical analysis the new method is compared with instance generation methods from the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

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 EPUB and 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

References

  1. Brockhoff, D., Saxena, D., Deb, K., Zitzler, E.: On handling a large number of objectives a posteriori and during optimization. In: Knowles, J., Corne, D., Deb, K., Chair, D. (eds.) Multiobjective Problem Solving from Nature. Natural Computing Series, pp. 377–403 (2008)

    Google Scholar 

  2. Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Genetic and Evolutionary Computation Conference, pp. 773–780 (2007)

    Google Scholar 

  3. Fisher, R.A.: Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika 10, 507–521 (1915)

    Google Scholar 

  4. 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 

  5. 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. Methods Appl. Mech. Eng. 196(4), 879–893 (2007)

    Article  MATH  Google Scholar 

  6. 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 

  7. 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 (2011)

    Google Scholar 

  8. 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 (2013)

    Google Scholar 

  9. Jaszkiewicz, A.: Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res. 137(1), 50–71 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Jozefowiez, N., Glover, F., Laguna, M.: Multi-objective meta-heuristics for the traveling salesman problem with profits. J. Math. Modell. Algor. 7(2), 177–195 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Knowles, J.D., Watson, R.A., Corne, D.W.: Reducing local optima in single-objective problems by multi-objectivization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 268–282. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  12. Knowles, J.D., Corne, D.W.: 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 

  13. 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 

  14. 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 

  15. Mavrovouniotis, M., Yang, S., Yao, X.: A benchmark generator for dynamic permutation-encoded problems. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 508–517. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., Neumann, F.: A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. 69(2), 151–182 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Moritz, R.L.V., Reich, E., Bernt, M., Middendorf, M.: The influence of correlated objectives on different types of P-ACO algorithms. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 230–241. Springer, Heidelberg (2014)

    Google Scholar 

  18. 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 (2010)

    Google Scholar 

  19. Nallaperuma, S., Wagner, M., Neumann, F.: Analyzing the effects of instance features and algorithm parameters for max-min ant system and the traveling salesperson problem. Frontiers in Robotics and AI 2(18) (2015)

    Google Scholar 

  20. 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  MathSciNet  MATH  Google Scholar 

  21. Reinelt, G.: TSPLIB-A traveling salesman problem library. ORSA J. Comput. 3(4), 376–384 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ridge, E., Kudenko, D.: Determining whether a problem characteristic affects heuristic performance. In: Cotta, C., van Hemert, J. (eds.) Recent Advances in Evol. Comp. SCI, vol. 153, pp. 21–35. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

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

    Chapter  Google Scholar 

  24. 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)

    Chapter  Google Scholar 

  25. 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  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruby L. V. Moritz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Moritz, R.L.V., Reich, E., Bernt, M., Middendorf, M. (2016). A Property Preserving Method for Extending a Single-Objective Problem Instance to Multiple Objectives with Specific Correlations. In: Chicano, F., Hu, B., García-Sánchez, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2016. Lecture Notes in Computer Science(), vol 9595. Springer, Cham. https://doi.org/10.1007/978-3-319-30698-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30698-8_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30697-1

  • Online ISBN: 978-3-319-30698-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics