Skip to main content

Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation

  • Conference paper
  • First Online:
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2023)

Abstract

This paper adapts a graph-based analysis and visualisation tool, search trajectory networks (STNs) to multi-objective combinatorial optimisation. We formally define multi-objective STNs and apply them to study the dynamics of two state-of-the-art multi-objective evolutionary algorithms: MOEA/D and NSGA2. In terms of benchmark, we consider two- and three-objective \(\rho \)mnk-landscapes for constructing multi-objective multi-modal landscapes with objective correlation. We find that STN metrics and visualisation offer valuable insights into both problem structure and algorithm performance. Most previous visual tools in multi-objective optimisation consider the objective space only. Instead, our newly proposed tool asses algorithm behaviour in the decision and objective spaces simultaneously.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

Notes

  1. 1.

    https://github.com/gabro8a/STNs.

  2. 2.

    http://45.32.184.82.

  3. 3.

    https://github.com/gabro8a/STNs-MOCO.

  4. 4.

    https://github.com/gabro8a/STNs-MOEA.

References

  1. Aguirre, H.E., Tanaka, K.: Working principles, behavior, and performance of MOEAs on MNK-landscapes. Eur. J. Oper. Res. 181(3), 1670–1690 (2007)

    Article  MATH  Google Scholar 

  2. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York (2007). https://doi.org/10.1007/978-0-387-36797-2

    Book  MATH  Google Scholar 

  3. Daolio, F., Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K.: Problem features versus algorithm performance on rugged multiobjective combinatorial fitness landscapes. Evol. Comput. 25(4), 555–585 (2017)

    Article  Google Scholar 

  4. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  6. Fieldsend, J.E., Alyahya, K.: Visualising the landscape of multi-objective problems using local optima networks. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2019, pp. 1421–1429. Association for Computing Machinery, New York (2019)

    Google Scholar 

  7. Fonseca, C.M., Fleming, P.J.: On the performance assessment and comparison of stochastic multiobjective optimizers. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61723-X_1022

    Chapter  Google Scholar 

  8. Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Exper. 21(11), 1129–1164 (1991)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  10. Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)

    Google Scholar 

  11. Kerschke, P., Grimme, C.: An expedition to multimodal multi-objective optimization landscapes. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 329–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54157-0_23

    Chapter  Google Scholar 

  12. Lavinas, Y., Aranha, C., Ochoa, G.: Search trajectories networks of multiobjective evolutionary algorithms. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds.) EvoApplications 2022. LNCS, vol. 13224, pp. 223–238. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02462-7_15

    Chapter  Google Scholar 

  13. Liefooghe, A., Daolio, F., Verel, S., Derbel, B., Aguirre, H., Tanaka, K.: Landscape-aware performance prediction for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 24(6), 1063–1077 (2020)

    Article  Google Scholar 

  14. Liefooghe, A., Derbel, B., Verel, S., López-Ibáñez, M., Aguirre, H., Tanaka, K.: On pareto local optimal solutions networks. In: Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11102, pp. 232–244. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99259-4_19

    Chapter  Google Scholar 

  15. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers (1999)

    Google Scholar 

  16. Ochoa, G., Tomassini, M., Verel, S., Verel, C.: A study of NK landscapes? Basins and local optima networks. In: Genetic and Evolutionary Computation Conference. GECCO, pp. 555–562. ACM Press, New York (2008)

    Google Scholar 

  17. Ochoa, G., Malan, K.M., Blum, C.: Search trajectory networks of population-based algorithms in continuous spaces. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 70–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43722-0_5

    Chapter  Google Scholar 

  18. Ochoa, G., Malan, K.M., Blum, C.: Search trajectory networks: a tool for analysing and visualising the behaviour of metaheuristics. Appl. Soft Comput. 109, 107492 (2021)

    Article  Google Scholar 

  19. Ochoa, G., Veerapen, N., Daolio, F., Tomassini, M.: Understanding phase transitions with local optima networks: number partitioning as a case study. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 233–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55453-2_16

    Chapter  Google Scholar 

  20. Schäpermeier, L., Grimme, C., Kerschke, P.: One PLOT to show them all: visualization of efficient sets in multi-objective landscapes. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 154–167. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58115-2_11

    Chapter  Google Scholar 

  21. Tušar, T., Filipič, B.: Visualization of Pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. 19(2), 225–245 (2015)

    Article  Google Scholar 

  22. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. 227(2), 331–342 (2013)

    Article  MathSciNet  Google Scholar 

  23. 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 

  24. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

We are deeply grateful to the SPECIES Society for funding a scholarship for Yuri Lavinas to visit the University of Stirling, Scotland, UK.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gabriela Ochoa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ochoa, G., Liefooghe, A., Lavinas, Y., Aranha, C. (2023). Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation. In: Pérez Cáceres, L., Stützle, T. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2023. Lecture Notes in Computer Science, vol 13987. Springer, Cham. https://doi.org/10.1007/978-3-031-30035-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30035-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30034-9

  • Online ISBN: 978-3-031-30035-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics