DESDEO: An Open Framework for Interactive Multiobjective Optimization

  • Vesa OjalehtoEmail author
  • Kaisa Miettinen
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 274)


We introduce a framework for interactive multiobjective optimization methods called DESDEO released under an open source license. With the framework, we want to make interactive methods easily accessible to be applied in solving real-world problems. The framework follows an object-oriented software design paradigm, where functionalities have been divided to modular, self-contained components. The framework contains implementations of some interactive methods, but also components which can be utilized to implement more interactive methods and, thus, increase the applicability of the framework. To demonstrate how the framework can be used, we consider an example problem where the pollution of a river is controlled. To solve this problem with four objectives, we apply two interactive methods called NAUTILUS and NIMBUS and show how the method can be switched during the solution process.


  1. Agrell, P. J., Lence, B. J., & Stam, A. (1998). An interactive multicriteria decision model for multipurpose reservoir management: The Shellmouth Reservoir. Journal of Multi-Criteria Decision Analysis, 7(2), 61–86.CrossRefGoogle Scholar
  2. Bechikh, S., Ben Said, L., & Ghedira, K. (2010). Estimating nadir point in multi-objective optimization using mobile reference points. In IEEE Congress on Evolutionary Computation (CEC) (pp. 1–9).Google Scholar
  3. Benayoun, R., de Montgolfier, J., Tergny, J., & Laritchev, O. (1971). Linear programming with multiple objective functions: Step method (STEM). Mathematical Programming, 1, 366–375.CrossRefGoogle Scholar
  4. Buchanan, J. T., & Corner, J. (1997). The effects of anchoring in interactive MCDM solution methods. Computers & Operations Research, 24(10), 907–918.CrossRefGoogle Scholar
  5. Chankong, V., & Haimes, Y. Y. (1983). Multiobjective decision making theory and methodology. New York: North-Holland.Google Scholar
  6. Deb, K., Miettinen, K., & Chaudhuri, S. (2010). Towards an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Transactions on Evolutionary Computation, 14(6), 821–841.CrossRefGoogle Scholar
  7. Durillo, J. J., & Nebro, A. J. (2011). Jmetal: A java framework for multi-objective optimization. Advances in Engineering Software, 42, 760–771.CrossRefGoogle Scholar
  8. Fowler, M. (2004). UML distilled: A brief guide to the standard object modeling language. Boston: Addison-Wesley Professional.Google Scholar
  9. Hakanen, J., Sahlstedt, K., & Miettinen, K. (2013). Wastewater treatment plant design and operation under multiple conflicting objective functions. Environmental Modelling & Software, 46(1), 240–249.CrossRefGoogle Scholar
  10. Hwang, C. L., & Masud, A. S. M. (1979). Multiple objective decision making, methods and applications: A state-of-the-art survey. Berlin: Springer.CrossRefGoogle Scholar
  11. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.CrossRefGoogle Scholar
  12. Kaliszewski, I. (2004). Out of the mist–towards decision-maker-friendly multiple criteria decision making support. European Journal of Operational Research, 158(2), 293–307.CrossRefGoogle Scholar
  13. Korhonen, P., Salo, S., & Steuer, R. E. (1997). A heuristic for estimating nadir criterion values in multiple objective linear programming. Operations Research, 45(5), 751–757.CrossRefGoogle Scholar
  14. Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., & Emmerich, M. (2017). Building and using an ontology of preference-based multiobjective evolutionary algorithms. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, & C. Grimme (Eds.), Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (pp. 406–421). Cham: Springer.CrossRefGoogle Scholar
  15. López-Ibáñez, M., & Knowles, J. (2015). Machine decision makers as a laboratory for interactive EMO. In A. Gaspar-Cunha, C. Henggeler Antunes, & C. C. Coello (Eds.), Evolutionary multi-criterion optimization. Lecture notes in computer science (pp. 295–309). Cham: Springer.Google Scholar
  16. Miettinen, K. (1999). Nonlinear multiobjective optimization. Dordrecht: Kluwer Academic Publishers.Google Scholar
  17. Miettinen, K. (2006). IND-NIMBUS for demanding interactive multiobjective optimization. In T. Trzaskalik (Ed.), Multiple Criteria Decision Making ’05 (pp. 137–150). Katowice: The Karol Adamiecki University of Economics in Katowice.Google Scholar
  18. Miettinen, K. (2007). Using interactive multiobjective optimization in continuous casting of steel. Materials and Manufacturing Processes, 22(5), 585–593.CrossRefGoogle Scholar
  19. Miettinen, K., Eskelinen, P., Ruiz, F., & Luque, M. (2010). NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point. European Journal of Operational Research, 206(2), 426–434.CrossRefGoogle Scholar
  20. Miettinen, K., & Hakanen, J. (2008). Why use interactive multi-objective optimization in chemical process design. In G. P. Rangaiah (Ed.), Multi-objective optimization: Techniques and applications in chemical engineering (pp. 153–188). Singapore: World Scientific.CrossRefGoogle Scholar
  21. Miettinen, K., & Mäkelä, M. M. (2000). Interactive multiobjective optimization system WWW-NIMBUS on the Internet. Computers & Operations Research, 27(7–8), 709–723.CrossRefGoogle Scholar
  22. Miettinen, K., & Mäkelä, M. M. (2002). On scalarizing functions in multiobjective optimization. OR Spectrum, 24(2), 193–213.CrossRefGoogle Scholar
  23. Miettinen, K., & Mäkelä, M. M. (2006). Synchronous approach in interactive multiobjective optimization. European Journal of Operational Research, 170(3), 909–922.CrossRefGoogle Scholar
  24. Miettinen, K., Mäkelä, M. M., & Männikkö, T. (1998). Optimal control of continuous casting by nondifferentiable multiobjective optimization. Computational Optimization and Applications, 11, 177–194.CrossRefGoogle Scholar
  25. Miettinen, K., Podkopaev, D., Ruiz, F., & Luque, M. (2015). A new preference handling technique for interactive multiobjective optimization without trading-off. Journal of Global Optimization, 63(4), 633–652.CrossRefGoogle Scholar
  26. Miettinen, K., & Ruiz, F. (2016). NAUTILUS framework: Towards trade-off-free interaction in multiobjective optimization. Journal of Business Economics, 86(1), 5–21.CrossRefGoogle Scholar
  27. Miettinen, K., Ruiz, F., & Wierzbicki, A. P. (2008). Introduction to multiobjective optimization: Interactive approaches. In J. Branke, K. Deb, K. Miettinen, & R. Slowinski (Eds.), Multiobjective optimization: Interactive and evolutionary approaches (pp. 27–57). Berlin: Springer.CrossRefGoogle Scholar
  28. Nakayama, H., Kaneshige, K., Takemoto, S., & Watada, Y. (1995). Application of a multi-objective programming technique to construction accuracy control of cable-stayed bridges. European Journal of Operational Research, 87(3), 731–738.CrossRefGoogle Scholar
  29. Nakayama, H., & Sawaragi, Y. (1984). Satisficing trade-off method for multiobjective programming. In M. Grauer, & A. P. Wierzbicki (Eds.), Interactive decision analysis (pp. 113–122). Berlin: Springer.CrossRefGoogle Scholar
  30. Narula, S., & Weistroffer, H. (1989). A flexible method for nonlinear multicriteria decision-making problems. IEEE Transactions on Systems, Man and Cybernetics, 19(4), 883–887.CrossRefGoogle Scholar
  31. Ojalehto, V., Miettinen, K., & Laukkanen, T. (2014). Implementation aspects of interactive multiobjective optimization for modeling environments: The case of GAMS-NIMBUS. Computational Optimization and Applications, 58(3), 757–779.CrossRefGoogle Scholar
  32. Ojalehto, V., Podkopaev, D., & Miettinen, K. (2016). Towards automatic testing of reference point based interactive methods. In J. Handl, E. Hart, R. P. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (pp. 483–492). Cham: Springer.CrossRefGoogle Scholar
  33. Oliphant, T. E. (2007). SciPy: Open source scientific tools for Python. Computing in Science and Engineering, 9, 10–20.CrossRefGoogle Scholar
  34. Ravaja, N., Korhonen, P., Köksalan, M., Lipsanen, J., Salminen, M., Somervuori, O., et al. (2016). Emotional–motivational responses predicting choices: The role of asymmetrical frontal cortical activity. Journal of Economic Psychology, 52, 56–70.CrossRefGoogle Scholar
  35. Ruiz, A. B., Sindhya, K., Miettinen, K., Ruiz, F., & Luque, M. (2015). E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method. European Journal of Operational Research, 246(1), 218–231.CrossRefGoogle Scholar
  36. Slee, M., Agarwal, A., & Kwiatkowski, M. (2007). Thrift: Scalable cross-language services implementation. Facebook White Paper, 5(8).Google Scholar
  37. Stam, A., Kuula, M., & Cesar, H. (1992). Transboundary air pollution in Europe: An interactive multicriteria tradeoff analysis. European Journal of Operational Research, 56(2), 263–277.CrossRefGoogle Scholar
  38. Storn, R., & Price, K. (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.CrossRefGoogle Scholar
  39. Szczepański, M., & Wierzbicki, A. (2003). Application of multiple criteria evolutionary algorithms to vector optimisation, decision support and reference point approaches. Journal of Telecommunications and Information Technology, 3, 16–33.Google Scholar
  40. Tarkkanen, S., Miettinen, K., Hakanen, J., & Isomäki, H. (2013). Incremental user-interface development for interactive multiobjective optimization. Expert Systems with Applications, 40, 3220–3232.CrossRefGoogle Scholar
  41. van Rossum, G. (1995). Python tutorial. Technical report, Centrum voor Wiskunde en Informatica (CWI).Google Scholar
  42. Wierzbicki, A. (1982). A mathematical basis for satisficing decision making. Mathematical Modelling, 3, 391–405.CrossRefGoogle Scholar
  43. Wierzbicki, A. P. (1980). The use of reference objectives in multiobjective optimization. In G. Fandel & T. Gal (Eds.), Multiple criteria decision making theory and application (pp. 468–486). Berlin: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of JyvaskylaFaculty of Information TechnologyJyväskyläFinland

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