MCDM'05 - paper no. 9


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IND-NIMBUS for demanding interactive multiobjective optimization

Kaisa Miettinen


In the paper IND-NIMBUS for Demanding Interactive Multiobjective Optimization (K. Miettinen) a new software package for solving demanding multi-objective optimization problems, named IN_NIMBUS, is introduced. Its main features and principles are described. The software package in question can be connection to different modeling and simulation tools and therefore it can be used to solve nonlinear, even non-convex problems, where the function evaluations require the solution of some underlying system, for example, a system of partial differential equations. In addition, the underlying interactive multi-objective optimization method NIMBUS and, in particular, its synchronous version are described.

Reference index:

Kaisa Miettinen, (2006), IND-NIMBUS for demanding interactive multiobjective optimization, Multiple Criteria Decision Making (1), pp. 137-150

Full text:


Scopus citations in 32 paper(s):
  1. Aittokoski, T., & Miettinen, K. (2008). Cost effective simulation-based multiobjective optimization in the performance of an internal combustion engine. Engineering Optimization, 40(7), 593-612. doi:10.1080/03052150801914429
  2. Ali, A. H., & Nazir, M. (2018). Radio resource management with QoS guarantees for LTE-A systems: A review focused on employing the multi-objective optimization techniques. Telecommunication Systems, 67(2), 349-365. doi:10.1007/s11235-017-0342-z
  3. Eskelinen, P., & Miettinen, K. (2012). Trade-off analysis approach for interactive nonlinear multiobjective optimization. OR Spectrum, 34(4), 803-816. doi:10.1007/s00291-011-0266-z
  4. Espírito Santo, I. A. C. P., Costa, L., & Fernandes, E. M. G. P. (2013). On optimizing a WWTP design using multi-objective approaches. Engineering Letters, 21(4), 193-202
  5. Giri, B. K., Hakanen, J., Miettinen, K., & Chakraborti, N. (2013). Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives. Applied Soft Computing Journal, 13(5), 2613-2623. doi:10.1016/j.asoc.2012.11.025
  6. Haara, A., Pykäläinen, J., Tolvanen, A., & Kurttila, M. (2018). Use of interactive data visualization in multi-objective forest planning. Journal of Environmental Management, 210, 71-86. doi:10.1016/j.jenvman.2018.01.002
  7. Hakanen, J., & Aittokoski, T. (2010). Comparison of MCDM and EMO approaches in wastewater treatment plant design doi:10.1007/978-3-642-01020-0_29
  8. Hakanen, J., Miettinen, K., & Sahlstedt, K. (2011). Wastewater treatment: New insight provided by interactive multiobjective optimization. Decision Support Systems, 51(2), 328-337. doi:10.1016/j.dss.2010.11.026
  9. Hakanen, J., Sahlstedt, K., & Miettinen, K. (2013). Wastewater treatment plant design and operation under multiple conflicting objective functions. Environmental Modelling and Software, 46, 240-249. doi:10.1016/j.envsoft.2013.03.016
  10. Hartikainen, M. E., Ojalehto, V., & Sahlstedt, K. (2015). Applying the approximation method PAINT and the interactive method NIMBUS to the multiobjective optimization of operating a wastewater treatment plant. Engineering Optimization, 47(3), 328-346. doi:10.1080/0305215X.2014.892593
  11. Hartikainen, M., Miettinen, K., & Wiecek, M. M. (2012). PAINT: Pareto front interpolation for nonlinear multiobjective optimization. Computational Optimization and Applications, 52(3), 845-867. doi:10.1007/s10589-011-9441-z
  12. Laukkanen, T., Tveit, T. -., Ojalehto, V., Miettinen, K., & Fogelholm, C. -. (2012). Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method. Applied Thermal Engineering, 48, 301-316. doi:10.1016/j.applthermaleng.2012.04.058
  13. Luque, M., Ruiz, F., & Miettinen, K. (2011). Global formulation for interactive multiobjective optimization. OR Spectrum, 33(1), 27-48. doi:10.1007/s00291-008-0154-3
  14. Miettinen, K. (2014). Survey of methods to visualize alternatives in multiple criteria decision making problems. OR Spectrum, 36(1), 3-37. doi:10.1007/s00291-012-0297-0
  15. Miettinen, K., & Hakanen, J. (2016). Why use interactive multi-objective optimization in chemical process design? Multi-objective optimization: Techniques and applications in chemical engineering (second edition) (pp. 157-198) doi:10.1142/9789812836526_0006
  16. Miettinen, K., Hakanen, J., & Podkopaev, D. (2016). Interactive nonlinear multiobjective optimization methods doi:10.1007/978-1-4939-3094-4_22
  17. Miettinen, K., Molina, J., González, M., Hernández-Díaz, A., & Caballero, R. (2009). Using box indices in supporting comparison in multiobjective optimization. European Journal of Operational Research,197(1), 17-24. doi:10.1016/j.ejor.2008.05.013
  18. Miettinen, K., Mustajoki, J., & Stewart, T. J. (2014). Interactive multiobjective optimization with NIMBUS for decision making under uncertainty. OR Spectrum, 36(1), 39-56. doi:10.1007/s00291-013-0328-5
  19. Ojalehto, V., & Miettinen, K. (2019). DESDEO: An open framework for interactive multiobjective optimization doi:10.1007/978-3-319-99304-1_3
  20. 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. doi:10.1007/s10589-014-9639-y
  21. Ruotsalainen, H., Miettinen, K., & Palmgren, J. -. (2010). Interactive multiobjective optimization for 3D HDR brachy therapy applying IND-NIMBUS doi:10.1007/978-3-642-10354-4_8
  22. Ruotsalainen, H., Miettinen, K., Palmgren, J. -., & Lahtinen, T. (2010). Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy. Physics in Medicine and Biology,55(16), 4703-4719. doi:10.1088/0031-9155/55/16/006
  23. Saccani, G., Hakanen, J., Sindhya, K., Ojalehto, V., Hartikainen, M., Antonelli, M., & Miettinen, K. (2020). Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process. Journal of Environmental Management, 254 doi:10.1016/j.jenvman.2019.109770
  24. Salehian, A. (2010). Interactive economic multi-objective optimization in electric power market. Paper presented at the 2010 IEEE PES Transmission and Distribution Conference and Exposition: Smart Solutions for a Changing World, doi:10.1109/TDC.2010.5484726
  25. Sindhya, K., Manninen, A., Miettinen, K., & Pippuri, J. (2017). Design of a permanent magnet synchronous generator using interactive multiobjective optimization. IEEE Transactions on Industrial Electronics, 64(12), 9776-9783. doi:10.1109/TIE.2017.2708038
  26. Sindhya, K., Ojalehto, V., Savolainen, J., Niemistö, H., Hakanen, J., & Miettinen, K. (2013). APROS-NIMBUS: Dynamic process simulator and interactive multiobjective optimization in plant automationdoi:10.1016/B978-0-444-63234-0.50146-9
  27. Sindhya, K., Ojalehto, V., Savolainen, J., Niemistö, H., Hakanen, J., & Miettinen, K. (2014). Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study. Expert Systems with Applications, 41(5), 2546-2558. doi:10.1016/j.eswa.2013.10.002
  28. Steponaviče, I., Ruuska, S., & Miettinen, K. (2014). A solution process for simulation-based multiobjective design optimization with an application in the paper industry. CAD Computer Aided Design, 47, 45-58. doi:10.1016/j.cad.2013.08.045
  29. Tarkkanen, S., Miettinen, K., Hakanen, J., & Isomäki, H. (2013). Incremental user-interface development for interactive multiobjective optimization. Expert Systems with Applications, 40(8), 3220-3232. doi:10.1016/j.eswa.2012.12.035
  30. Wägemann, T., Tavakoli Kolagari, R., & Schmid, K. (2019). ADOOPLA - combining product-line- and product-level criteria in multi-objective optimization of product line architectures doi:10.1007/978-3-030-29983-5_9
  31. Yuan, L., Wan, Z., & Chen, J. (2012). A filled function algorithm for multiobjective optimization. WSEAS Transactions on Mathematics, 11(8), 675-683
  32. Zhou-Kangas, Y., Miettinen, K., & Sindhya, K. (2019). Solving multiobjective optimization problems with decision uncertainty: An interactive approach. Journal of Business Economics, 89(1), 25-51. doi:10.1007/s11573-018-0900-1