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Research Article

Task-based visual analytics for interactive multiobjective optimization

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Pages 2073-2090 | Received 10 Jan 2020, Accepted 11 May 2020, Published online: 16 Jun 2020

References

  • Amar, R., & Stasko, J. (2004). Knowledge task-based framework for design and evaluation of information visualizations. 2004 IEEE Symposium on Information Visualization, (pp. 143–150). IEEE. https://doi.org/https://doi.org/10.1109/INFVIS.2004.10
  • Berger, W., Piringer, H. (2010). Interactive visual analysis of multiobjective optimizations. In Proceedings of 2010 IEEE Symposium on Visual Analytics Science and Technology (p. 215–216). IEEE.
  • Bernold, G., Matkovic, K., Gröller, M. E., & Raidou, R. G. (2019). preha: Establishing precision rehabilitation with visual analytics. In B. Kozlíková, L. Linsen, & P.-P. Vázquez (Eds.), Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine (pp. 79–89). The Eurographics Association.
  • Card, S. K., Robertson, G. G., Mackinlay, J. D. (1991). The information visualizer, an information workspace. In Proceedings of the Sigchi Conference on Human Factors in Computing Systems (pp. 181–186). ACM.
  • Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2001). Scalable test problems for evolutionary multi-objective optimization. In A. Abraham, L. Jain, & R. Goldberg (Eds.), Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing (p. 105–145). Springer.
  • Doleisch, H., Gasser, M., & Hauser, H. (2003). Interactive feature specification for focus + context visualization of complex simulation data. In Proceedings of the Symposium on Data Visualisation 2003 (pp. 239–248). Eurographics Association.
  • Eskelinen, P., Miettinen, K., Klamroth, K., & Hakanen, J. (2010). Pareto Navigator for interactive nonlinear multiobjective optimization. OR Spectrum, 32(1), 211–227. https://doi.org/https://doi.org/10.1007/s00291-008-0151-6
  • Gardiner, L. R., & Steuer, R. E. (1994a). Unified interactive multiple objective programming. European Journal of Operational Research, 74(3), 391–406. https://doi.org/https://doi.org/10.1016/0377-2217(94)90219-4
  • Gardiner, L. R., & Steuer, R. E. (1994b). Unified interactive multiple objective programming: An open architecture for accommodating new procedures. Journal of the Operational Research Society, 45(12), 1456–1466. https://doi.org/https://doi.org/10.2307/2583939
  • Gresh, D. L., Rogowitz, B. E., Winslow, R. L., Scollan, D. F., Yung, C. K. (2000). WEAVE: A system for visually linking 3-D and statistical visualizations, applied to cardiac simulation and measurement data. In Proceedings of the IEEE Visualization Conference (VIS 2000) (pp. 489–492).
  • Hakanen, J., Sahlstedt, K., & Miettinen, K. (2013). Wastewater treatment plant design and operation under multiple conflicting objective functions. Environmental Modelling & Software, 46, 240–249. https://doi.org/https://doi.org/10.1016/j.envsoft.2013.03.016
  • Hartikainen, M., Miettinen, K., & Klamroth, K. (2019). Interactive nonconvex Pareto Navigator for multiobjective optimization. European Journal of Operational Research, 275(1), 238–251. https://doi.org/https://doi.org/10.1016/j.ejor.2018.11.038
  • Hauser, H., et al. (2006). Generalizing focus + context visualization. In G.-P. Bonneau (Ed.), Scientific Visualization: The visual extraction of knowledge from data (pp. 305–327). Springer.
  • Hauser, H., Ledermann, F., Doleisch, H. (2002). Angular brushing of extended parallel coordinates. In Proceedings of the IEEE Symposium on Information Visualization (INFOVIS 2002) (pp. 127–130).
  • He, Z., & Yen, G. G. (2016). Visualization and performance metric in many-objective optimization. IEEE Transactions on Evolutionary Computation, 20(3), 386–402. https://doi.org/https://doi.org/10.1109/TEVC.2015.2472283
  • Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. ACM Queue, 10(2), 30–55. https://doi.org/https://doi.org/10.1145/2133416.2146416
  • Hettenhausen, J., Lewis, A., & Mostaghim, S. (2010). Interactive multi-objective particle swarm optimization with heatmap-visualization-based user interface. Engineering Optimization, 42(2), 119–139. https://doi.org/https://doi.org/10.1080/03052150903042632
  • Hwang, C.-L., & Masud, A. S. M. (1979). Multiple objective decision making – methods and applications: a state-of-the-art survey. Springer.
  • Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016). 3D-RADVIS: Visualization of Pareto front in many-objective optimization [Paper presentation]. 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 736–745). IEEE. https://doi.org/https://doi.org/10.1109/CEC.2016.7743865
  • Jaszkiewicz, A., & Branke, J. (2008). Interactive multiobjective evolutionary algorithms. In J. Branke, K. Deb, K. Miettinen, & R. Slowinski (Eds.), Multiobjective optimization: interactive and evolutionary approaches (pp. 179–194). Springer.
  • Jaszkiewicz, A., & Słowiński, R. (1999). The ‘light beam search’ approach – an overview of methodology and applications. European Journal of Operational Research, 113(2), 300–314. https://doi.org/https://doi.org/10.1016/S0377-2217(98)00218-5
  • 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. https://doi.org/https://doi.org/10.1016/j.ejor.2003.06.005
  • Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In A. Kerren, J. T. Stasko, J.-T. Fekete, C. Görg, J. Kohlhammer, & G. Melançon (Eds.), Information visualization: Human-centered issues and perspectives (pp. 154–175). Springer.
  • Kerren, A., & Schreiber, F. (2012). Toward the role of interaction in visual analytics. In C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, & A. M. Uhrmacher (Eds.), Proceedings of the 2012 Winter Simulation Conference (vol. 420; pp. 1–420). IEEE.
  • Konyha, Z., Lež, A., Matković, K., Jelović, M., & Hauser, H. (2012). Interactive visual analysis of families of curves using data aggregation and derivation [Paper presentation]. The 12th international conference, In Proceedings of Knowledge Management and Knowledge Technologies (pp. 24:1–24:8). ACM. https://doi.org/https://doi.org/10.1145/2362456.2362487
  • Korhonen, P. (2005). Interactive methods. In J. Figueira, S. Greco, & M. Ehrgott (Eds.), Multiple criteria decision analysis: state of the art surveys (pp. 641–661). Springer.
  • Lee, B., Plaisant, C., Parr, C. S., Fekete, J.-D., & Henry, N. (2006). Task taxonomy for graph visualization [Paper presentation]. The 2006 AVI workshop, Proceedings of on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (pp. 1–5). ACM.
  • Lotov, A. V., Bushenkov, V. A., & Kamenev, G. K. (2004). Interactive decision maps: approximation and visualization of Pareto frontier. Kluwer Academic Publishers.
  • Lotov, A. V., & Miettinen, K. (2008). Visualizing the Pareto frontier. In J. Branke, K. Deb, K. Miettinen, & R. Slowinski (Eds.), Multiobjective optimization: interactive and evolutionary approaches (pp. 213–243). Springer.
  • Luque, M., Ruiz, F., & Miettinen, K. (2011). Global formulation for interactive multiobjective optimization. OR Spectrum, 33(1), 27–48. https://doi.org/https://doi.org/10.1007/s00291-008-0154-3
  • Luque, M., Yang, J. B., & Wong, B., Y. (2009). PROJECT method for multiobjective optimization based on gradient projection and reference points. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39(4), 864–879. https://doi.org/https://doi.org/10.1109/TSMCA.2009.2019855
  • Matkovic, K., Freiler, W., Gracanin, D., Hauser, H. (2008). ComVis: A coordinated multiple views system for prototyping new visualization technology. In Proceedings of the 12th International Conference on Information Visualisation (p. 215–220).
  • 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.
  • Miettinen, K. (1999). Nonlinear multiobjective optimization. Kluwer Academic Publishers.
  • Miettinen, K. (2014). Survey of methods to visualize alternatives in multiple criteria decision making problems. OR Spectrum, 36(1), 3–37. https://doi.org/https://doi.org/10.1007/s00291-012-0297-0
  • Miettinen, K., Hakanen, J., & Podkopaev, D. (2016). Interactive nonlinear multiobjective optimization methods. In S. Greco, M. Ehrgott, & J. Figueira (Eds.), Multiple criteria decision analysis: state of the art surveys (2nd ed., pp. 931–980). Springer.
  • Miettinen, K., & Mäkelä, M. M. (2006). Synchronous approach in interactive multiobjective optimization. European Journal of Operational Research, 170(3), 909–922. https://doi.org/https://doi.org/10.1016/j.ejor.2004.07.052
  • 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). Springer.
  • Munzner, T. (2014). Visualization analysis and design. Taylor & Francis.
  • Nakayama, H., & Furukawa, K. (1985). Satisficing trade-off method with an application to multiobjective structural design. Large Scale Systems, 8, 47–57.
  • North, C., & Shneiderman, B. (2000). Snap-together visualization: A user interface for coordinatingvisualizations via relational schemata. In Proceedings of the working conference on advanced visual interfaces (pp. 128–135). ACM.
  • 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. https://doi.org/https://doi.org/10.1007/s10589-014-9639-y
  • Poles, S., Vassileva, M., & Sasaki, D. (2008). Multiobjective optimization software. In J. Branke, K. Deb, K. Miettinen, & R. Slowinski (Eds.), Multiobjective optimization: interactive and evolutionary approaches (pp. 329–348). Springer.
  • Purshouse, R., Deb, K., Mansor, M., Mostaghim, S., & Wang, R. (2014). A review of hybrid evolutionary multiple criteria decision making methods [Paper presentation]. 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 1147–1154). IEEE. https://doi.org/https://doi.org/10.1109/CEC.2014.6900368
  • Radoš, S., Splechtna, R., Matković, K., Đuras, M., Gröller, E., & Hauser, H. (2016). Towards quantitative visual analytics with structured brushing and linked statistics. Computer Graphics Forum, 35(3), 251–260. https://doi.org/https://doi.org/10.1111/cgf.12901
  • Roberts, J. C. (2007). State of the art: Coordinated multiple views in exploratory visualization. In Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV ‘07) (pp. 61–71). IEEE.
  • Ruiz, F., Luque, M., & Miettinen, K. (2012). Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization. Annals of Operations Research, 197(1), 47–70. https://doi.org/https://doi.org/10.1007/s10479-010-0831-x
  • Steuer, R. E., & Choo, E.-U. (1983). An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical Programming, 26(3), 326–344. https://doi.org/https://doi.org/10.1007/BF02591870
  • Stewart, T., Bandte, O., Braun, H., Chakraborti, N., Ehrgott, M., & Göbelt, M. (2008). Real-world applications of multiobjective optimization. In J. Branke, K. Deb, K. Miettinen, & R. Slowinski (Eds.), Multiobjective optimization: interactive and evolutionary approaches (pp. 285–327). Springer.
  • 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. https://doi.org/https://doi.org/10.1016/j.eswa.2012.12.035
  • Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society.
  • Trinkaus, H. L., & Hanne, T. (2005). knowCube: A visual and interactive support for multicriteria decision making. Computers and Operations Research, 32(5), 1289–1309. https://doi.org/https://doi.org/10.1016/j.cor.2003.11.010
  • Tusar, T., & Filipic, B. (2015). Visualization of Pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method. IEEE Transactions on Evolutionary Computation, 19(2), 225–245. https://doi.org/https://doi.org/10.1109/TEVC.2014.2313407
  • Valiati, E. R. A., Pimenta, M. S., & Freitas, C. M. D. S. (2006). A taxonomy of tasks for guiding the evaluation of multidimensional visualizations [Paper presentation]. The 2006 AVI Workshop, Proceedings of on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (pp. 1–6). ACM. https://doi.org/https://doi.org/10.1145/1168149.1168169
  • Wang, R., Purshouse, R. C., & Fleming, P. J. (2013). “Whatever Works Best for You”- a new method for a priori and progressive multi-objective optimisation. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, & J. Shaw (Eds.), Evolutionary multi-criterion optimization: 7th international conference, EMO 2013, proceedings (pp. 337–351). Springer.
  • Weistroffer, H. R., & Narula, S. C. (1997). The state of multiple criteria decision support software. Annals of Operations Research, 72, 299–313. https://doi.org/10.1023/A:1018956506912
  • Wierzbicki, A. P., Makowski, M., & Wessels, J. (Eds.). (2000). Model-based decision support methodology with environmental applications. Kluwer Academic Publishers.
  • Wierzbicki, A. P. (1982). A mathematical basis for satisficing decision making. Mathematical Modelling, 3(5), 391–405. https://doi.org/https://doi.org/10.1016/0270-0255(82)90038-0
  • Wierzbicki, A. P. (1999). Reference point approaches. In T. Gal, T. J. Stewart, & T. Hanne (Eds.), Multicriteria decision making: advances in mcdm models, algorithms, theory, and applications (pp. 9–1–9-39). Kluwer Academic Publishers.
  • Woodruff, M. J., Reed, P. M., & Simpson, T. W. (2013). Many objective visual analytics: Rethinking the design of complex engineered systems. Structural and Multidisciplinary Optimization, 48(1), 201–219. https://doi.org/https://doi.org/10.1007/s00158-013-0891-z
  • Yi, J. S., Kang, Y., Stasko, J. T., & Jacko, J. A. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1224–1231. https://doi.org/https://doi.org/10.1109/TVCG.2007.70515

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