scholarly journals Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods

2018 ◽  
pp. 274-285 ◽  
Author(s):  
Cristóbal Barba-González ◽  
Vesa Ojalehto ◽  
José García-Nieto ◽  
Antonio J. Nebro ◽  
Kaisa Miettinen ◽  
...  
Keyword(s):  
Decision Maker ◽  
Reference Point ◽  
Interactive Methods ◽  
Testing Reference

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

scholarly journals Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker

2021 ◽  
Author(s):  
Bekir Afsar ◽  
Ana B. Ruiz ◽  
Kaisa Miettinen
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Decision Makers ◽  
Interactive Methods ◽  
Preference Information ◽  
Evolutionary Methods ◽  
Solution Processes ◽  
Trade Offs

AbstractSolving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different interactive methods, and it is important to compare them and find the best-suited one for solving the problem in question. Comparisons with real decision makers are expensive, and artificial decision makers (ADMs) have been proposed to simulate humans in basic testing before involving real decision makers. Existing ADMs only consider one type of preference information. In this paper, we propose ADM-II, which is tailored to assess several interactive evolutionary methods and is able to handle different types of preference information. We consider two phases of interactive solution processes, i.e., learning and decision phases separately, so that the proposed ADM-II generates preference information in different ways in each of them to reflect the nature of the phases. We demonstrate how ADM-II can be applied with different methods and problems. We also propose an indicator to assess and compare the performance of interactive evolutionary methods.

scholarly journals A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

2015 ◽  
Vol 10 (4) ◽  
pp. 508 ◽  
Author(s):  
Ernestas Filatovas ◽  
Dmitry Podkopaev ◽  
Olga Kurasova
Keyword(s):  
Multiobjective Optimization ◽  
Dimensionality Reduction ◽  
Decision Maker ◽  
Optimization Problem ◽  
Interactive Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Visualization Technique ◽  
Preference Information ◽  
Optimal Set

<pre>Interactive methods of <span>multiobjective</span> optimization repetitively derive <span>Pareto</span> optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the <span>Pareto</span> optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. We propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. We describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method NIMBUS.</pre>

scholarly journals MULTI-OBJECTIVE OPTIMIZATION FOR FACILITIES MANAGEMENT

2004 ◽  
Vol 5 (4) ◽  
pp. 173-182 ◽  
Author(s):  
Willem K. Brauers
Keyword(s):  
Decision Maker ◽  
Reference Point ◽  
Creative Thinking ◽  
Point Theory ◽  
Facilities Management ◽  
Multi Objective Optimization ◽  
Dimensionless Numbers ◽  
Short Text ◽  
Multi Objective ◽  
Desk Research

The main point of this article is to present in a short text all aspects of Multi ‐ Objective Optimization for Facilities Management, so to say from the cradle until the grave. Additionally, the combination of Multi ‐ Objective Optimization with Nominal Methods and Scenario Writing represents an innovation. It is also stated that all stakeholders interested in the issue, instead of one decision maker, have to be involved. First, desk research will discover all the surrounding conditions of the issue under consideration. Therefore, during a period of creative thinking in a nominal exercise all the main influencing events are recorded and finally ranked. From this information, scenarios for the future of the facilities sector are deduced. On basis of all these data, objectives and alternatives are simulated. A Multi ‐ Objective Optimization for the facilities sector is made possible by two methods: an additive method with ratios and the application of Reference Point Theory. Automatically, using these methods, all objectives are normalized to dimensionless numbers between zero and one. Nevertheless, a problem of importance for each objective may remain. Therefore, two methods are proposed. First, weights are granted in a nonlinear way. Secondly, an objective becomes more important by introducing different attributes for the same objective. The latter method seems to be more refined. In this way, a final ranking of the alternatives for the fulfillment of the objectives is obtained.

scholarly journals Surrogate assisted interactive multiobjective optimization in energy system design of buildings

2021 ◽  
Author(s):  
Pouya Aghaei Pour ◽  
Tobias Rodemann ◽  
Jussi Hakanen ◽  
Kaisa Miettinen
Keyword(s):  
Decision Maker ◽  
Management Strategy ◽  
Computation Time ◽  
Energy System ◽  
Surrogate Models ◽  
Model Management ◽  
Interactive Methods ◽  
Objective Functions ◽  
Interactive Method ◽  
Computationally Expensive

AbstractIn this paper, we develop a novel evolutionary interactive method called interactive K-RVEA, which is suitable for computationally expensive problems. We use surrogate models to replace the original expensive objective functions to reduce the computation time. Typically, in interactive methods, a decision maker provides some preferences iteratively and the optimization algorithm narrows the search according to those preferences. However, working with surrogate models will introduce some inaccuracy to the preferences, and therefore, it would be desirable that the decision maker can work with the solutions that are evaluated with the original objective functions. Therefore, we propose a novel model management strategy to incorporate the decision maker’s preferences to select some of the solutions for both updating the surrogate models (to improve their accuracy) and to show them to the decision maker. Moreover, we solve a simulation-based computationally expensive optimization problem by finding an optimal configuration for an energy system of a heterogeneous business building complex. We demonstrate how a decision maker can interact with the method and how the most preferred solution is chosen. Finally, we compare our method with another interactive method, which does not have any model management strategy, and shows how our model management strategy can help the algorithm to follow the decision maker’s preferences.

Cognitive Reflection Effects on Time Discounting

2018 ◽  
Vol 39 (2) ◽  
pp. 99-106 ◽  
Author(s):  
Michał Białek ◽  
Przemysław Sawicki
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Delay Discounting ◽  
Reference Point ◽  
Reference Points ◽  
Cognitive Reflection Test ◽  
Process Information ◽  
Time Discounting ◽  
Cognitive Reflection ◽  
The Way

Abstract. In this work, we investigated individual differences in cognitive reflection effects on delay discounting – a preference for smaller sooner over larger later payoff. People are claimed to prefer more these alternatives they considered first – so-called reference point – over the alternatives they considered later. Cognitive reflection affects the way individuals process information, with less reflective individuals relying predominantly on the first information they consider, thus, being more susceptible to reference points as compared to more reflective individuals. In Experiment 1, we confirmed that individuals who scored high on the Cognitive Reflection Test discount less strongly than less reflective individuals, but we also show that such individuals are less susceptible to imposed reference points. Experiment 2 replicated these findings additionally providing evidence that cognitive reflection predicts discounting strength and (in)dependency to reference points over and above individual difference in numeracy.

Sign in / Sign up

Export Citation Format

Share Document