Assessing the Performance of Interactive Multiobjective Optimization Methods

Afşar, Bekir; Miettinen, Kaisa; Ruiz, Francisco

doi:10.1145/3448301

Cited by 35 publications

(29 citation statements)

References 73 publications

(222 reference statements)

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“…The process is repeated until a satisfactory solution (or solutions) is/are found. The decision making can be performed by humans and/or by computer algorithms [ 21 ].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Optimization of Polymer Processing: A Review (Part I—Extrusion)

2022

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Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related to the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems and define the best procedure for each particular practical situation. For this purpose, the state of the art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focuses on extrusion, namely single and twin-screw extruders, extrusion dies, and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing with a very good performance and without the need of demanding computation requirements.

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“…The process is repeated until a satisfactory solution (or solutions) is/are found. The decision making can be performed by humans and/or by computer algorithms [ 21 ].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Optimization of Polymer Processing: A Review (Part I—Extrusion)

2022

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“…Thus, a physical user interface can give the DM a better feeling of control in providing preferences and directing the solution process. Feeling in control is a desirable property in interactive algorithms, as noted in a recent study [1]. A physical user interface also has applications in real-time interactive optimization (e.g., power station control, stock markets, etc.…”

Section: Introductionmentioning

confidence: 99%

“…. , 𝑓 𝐾 (x)}, subject to x ∈ Ω, (1) where Ω is the feasible region of the decision space R 𝑛 . The corresponding objective vector for a feasible decision vector x is f(x), and consists of the objective (function) values (𝑓 1 (x), .…”

Section: Introductionmentioning

confidence: 99%

“…Such a wide variety of preferences are available so that the DM can use an appropriate type that makes him/her comfortable while solving the MOP interactively. As concluded in the survey [1], previous works have not given enough attention to user interfaces, the way the DM provides the preferences, or which preference will be advantageous for the DM. To our knowledge, the past works on user interfaces were primarily focused on visualization techniques and not on input devices in the context of multiobjective optimization and decision making.…”

Section: Introductionmentioning

confidence: 99%

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Interactive evolutionary multiobjective optimization with modular physical user interface

Mazumdar

Otayagich

Miettinen

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Incorporating the preferences of a domain expert, a decision-maker (DM), in solving multiobjective optimization problems increased in popularity in recent years. The DM can choose to use different types of preferences depending on his/her comfort, requirements, or the problem being solved. Most papers, where preference-based and interactive algorithms have been proposed, do not pay attention to the user interfaces and input devices. If they do, they use character or graphics-based preference input methods. We propose the option of using a physical or tactile input device that gives the DM a better sense of control over providing his/her preferences.However, off the shelf hardware devices are not tailored to solve multiobjective optimization problems and provide many controls that may increase the cognitive load on the DM. In this paper, we propose a fully modular physical user interface to input preference information for solving multiobjective optimization problems. The modularity allows to arrange each input module in various ways depending on the algorithm, DM's requirements, or the problem being solved. The device can be used with any computer and uses web-based visualizations. We demonstrate the potential of the physical interface by solving a real-world problem with an interactive decomposition-based multiobjective evolutionary algorithm.

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“…Interactive methods seem to be related to the change of the preference due to the interaction between the optimization algorithm and the DM, but its purpose is to find the optimal solution most preferred by the DM. When the DM is satisfied with a solution, the decision maker can terminate the algorithm immediately [29]. However, the dynamic reference point model focuses on the change of the preference, which requires the algorithm to be able to track changes in preferences and find the Pareto optimal solution under each preference as fast as possible.…”

Section: Introductionmentioning

confidence: 99%

A novel dynamic reference point model for preference-based evolutionary multiobjective optimization

Lin

Luo

et al. 2022

Complex Intell. Syst.

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In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (ĝa-NSCSA) and a genetic algorithm (ĝa-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that ĝa-NSCSA exhibits better performance among the compared optimization algorithms.

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Assessing the Performance of Interactive Multiobjective Optimization Methods

Cited by 35 publications

References 73 publications

Optimization of Polymer Processing: A Review (Part I—Extrusion)

Optimization of Polymer Processing: A Review (Part I—Extrusion)

Interactive evolutionary multiobjective optimization with modular physical user interface

A novel dynamic reference point model for preference-based evolutionary multiobjective optimization

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