FiDiPro Project DeCoMo: Decision Support for Complex Multiobjective Optimization Problems

    Publications in the project

  1. T. Chugh , Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, to appear, doi: 10.1109/TEVC.2016.26 22301
  2. T. Chugh , N. Chakraborti, K. Sindhya, and Y. Jin. A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem, Materials and Manufacturing Processes, 32, 1172-1178, 2017
  3. J. Hakanen and J. Knowles. On using decision maker preferences with ParEGO. In "EMO 2017", Springer, 282-297, 2017, EMO 2017, 9th International Conference on Evolutionary Multi-Criterion Optimization, Springer International Publishing, 282-297, 2017
  4. J. Hakanen, T. Chugh , K. Sindhya, Y. Jin and K.Miettinen. Connections of Reference Vectors and Different Types of Preference Information in Interactive Multiobjective Evolutionary Algorithms , in "IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)", IEEE, 1-8, 2016
  5. T. Chugh, K. Sindhya, K.Miettinen, J. Hakanen and Y. Jin. On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization , in "14th International Conference on Parallel Problem Solving from Nature, Edinburg, UK, 2016 Proceedings", Springer International Publishing, 214-224, 2016 (open access)
  6. Other Related publications

  7. Tabatabaei, M., Hakanen, J., Hartikainen, M., Miettinen, K., Sindhya, K, A Survey on Handling Computationally Expensive Multiobjective Optimization Problems using Surrogates: Non-Nature Inspired Methods, Structural and Multidisciplinary Optimization, 52, 1-25, 2015 Published in parallel in JYX (open access)
  8. R. Cheng, Y. Jin and K. Narukawa. Adaptive reference vector generation for inverse model based evolutionary multiobjective optimization with degenerate and disconnected Pareto fronts. The 8th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO'2015), Guimarães, Portugal, 2015
  9. R. Cheng, Y. Jin, K. Narukawa and B. Sendhoff. A multiobjective evolutionary algorithm using Gaussian process based inverse modeling. IEEE Transactions on Evolutionary Computation, 2015 (accepted)
  10. Sindhya, K., Ojalehto, V., Savolainen, J., Niemistö, H., Hakanen, J., Miettinen, K., Coupling Dynamic Simulation and Interactive Multiobjective Optimization for Complex Problems: An APROS-NIMBUS Case Study, Expert Systems with Applications, 41(5), 2546-2558, 2014.
  11. Steponavice, I., Ruuska, S., Miettinen, K., A Solution Process for Simulation-based Multiobjective Design Optimization with an Application in Paper Industry, Computer-Aided Design, 47, 45-58, 2014.
  12. Ojalehto, V., Miettinen, K., Laukkanen, T., Implementation Aspects of Interactive Multiobjective Optimization for Modeling Environments: The Case of GAMS-NIMBUS, Computational Optimization and Applications, 58(3), 757-779, 2014.
  13. X. Zhang, Y. Tian and Y. Jin. A knee point driven evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 2014 (accepted)
  14. Miettinen, K., Mustajoki, J., Stewart T.J., Interactive Multiobjective Optimization with NIMBUS for Decision Making under Uncertainty, OR Spectrum, 36(1), 39-56, 2014.
  15. C. Sun, Y. Jin, J. Zeng and Y. Yu. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Computing, 2014 (accepted)
  16. Sindhya, K., Miettinen, K., Deb, K., A Hybrid Framework for Evolutionary Multi-Objective Optimization, IEEE Transactions on Evolutionary Computation, 17(4), 495-511, 2013.
  17. X. Sun, D. Gong, Y. Jin and S. Chen. A new surrogate-assisted interactive genetic algorithm with weighted semi-supervised learning. IEEE Transactions on Cybernetics, 43(2):685-698, 2013
  18. Hartikainen, M., Miettinen, K., Wiecek, M.M., PAINT: Pareto Front Interpolation for Nonlinear Multiobjective Optimization, Computational Optimization and Applications, 52(3), 845-867, 2012.
  19. Y. Jin. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation. 1(2):61-70, 2011
  20. D. Lim, Y. Jin, Y.-S. Ong, and B. Sendhoff. Generalizing surrogate-assisted evolutionary computation. IEEE Transactions on Evolutionary Computation, 14(3):329-355, 2010
  21. Eskelinen, P., Miettinen, K., Klamroth, K., Hakanen, J., Pareto Navigator for Interactive Nonlinear Multiobjective Optimization, OR Spectrum, 23, 211-227, 2010.
  22. Miettinen, K., Eskelinen, P., Ruiz, F., Luque, M., NAUTILUS Method: An Interactive Technique in Multiobjective Optimization based on the Nadir Point, European Journal of Operational Research, 206(2), 426-434, 2010.
  23. Thiele, L., Miettinen, K., Korhonen, P. Molina, J., A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization, Evolutionary Computation, 17(3), 411-436, 2009.
  24. Miettinen, K., Mäkelä, M.M., Synchronous Approach in Interactive Multiobjective Optimization, European Journal of Operational Research, 170(3), 909-922, 2006.
  25. Miettinen, K., Nonlinear Multiobjective Optimization , Kluwer Academic Publishers, Boston, 1999.