Why Use Interactive Multi-Objective Optimization in Chemical Process Design?
Miettinen, K., & Hakanen, J. (2017). Why Use Interactive Multi-Objective Optimization in Chemical Process Design?. In G. P. Rangaiah (Ed.), Multi-Objective Optimization : Techniques and Applications in Chemical Engineering (2nd ed., pp. 157-197). World Scientific. Advances in Process Systems Engineering, 5. https://doi.org/10.1142/9789813148239_0006
Julkaistu sarjassa
Advances in Process Systems EngineeringToimittajat
Päivämäärä
2017Tekijänoikeudet
© World Scientific Publishing Co, 2017. This is a final draft version of an article whose final and definitive form has been published by World Scientific. Published in this repository with the kind permission of the publisher.
Problems in chemical engineering, like most real-world optimization
problems, typically, have several conflicting performance criteria or objectives
and they often are computationally demanding, which sets special
requirements on the optimization methods used. In this chapter,
we point out some shortcomings of some widely used basic methods of
multi-objective optimization. As an alternative, we suggest using interactive
approaches where the role of a decision maker or a designer is
emphasized. Interactive multi-objective optimization has been shown to
suit well for chemical process design problems because it takes the preferences
of the decision maker into account in an iterative manner that
enables a focused search for the best Pareto optimal solution, that is,
the best compromise between the conflicting objectives. For this reason,
only those solutions that are of interest to the decision maker need to
be generated making this kind of an approach computationally efficient.
Besides, the decision maker does not have to compare many solutions at
a time which makes interactive approaches more usable from the cognitive
point of view. Furthermore, during the interactive solution process
the decision maker can learn about the interrelationships among the
objectives. In addition to describing the general philosophy of interactive
approaches, we discuss the possibilities of interactive multi-objective
optimization in chemical process design and give some examples of interactive
methods to illustrate the ideas. Finally, we demonstrate the
usefulness of interactive approaches in chemical process design by summarizing
some reported studies related to, for example, paper making
and sugar industries. Let us emphasize that the approaches described
are appropriate for problems with more than two objective functions.
...
Julkaisija
World ScientificEmojulkaisun ISBN
978-981-3148-22-2Kuuluu julkaisuun
Multi-Objective Optimization : Techniques and Applications in Chemical EngineeringAsiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/27831823
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Why Use Interactive Multi-Objective Optimization in Chemical Process Design?
Miettinen, Kaisa; Hakanen, Jussi (World Scientific, 2009)Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements ... -
Designing empirical experiments to compare interactive multiobjective optimization methods
Afsar, Bekir; Silvennoinen, Johanna; Misitano, Giovanni; Ruiz, Francisco; Ruiz, Ana B.; Miettinen, Kaisa (Palgrave Macmillan, 2022)Interactive multiobjective optimization methods operate iteratively so that a decision maker directs the solution process by providing preference information, and only solutions of interest are generated. These methods ... -
Interactive Nonlinear Multiobjective Optimization Methods
Miettinen, Kaisa; Hakanen, Jussi; Podkopaev, Dmitry (Springer Science+Business Media, 2016)An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory ... -
Interactive Multiobjective Robust Optimization with NIMBUS
Zhou-Kangas, Yue; Miettinen, Kaisa; Sindhya, Karthik (Springer, 2018)In this paper, we introduce the MuRO-NIMBUS method for solving multiobjective optimization problems with uncertain parameters. The concept of set-based minmax robust Pareto optimality is utilized to tackle the uncertainty ... -
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
Misitano, Giovanni; Saini, Bhupinder Singh; Afsar, Bekir; Shavazipour, Babooshka; Miettinen Kaisa (Institute of Electrical and Electronics Engineers (IEEE), 2021)Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the ...
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