https://www.dagstuhl.de/23361

03. – 08. September 2023, Dagstuhl-Seminar 23361

Multiobjective Optimization on a Budget

Organisatoren

Richard Allmendinger (University of Manchester, GB)
Carlos M. Fonseca (University of Coimbra, PT)
Serpil Sayin (Koc University – Istanbul, TR)
Margaret M. Wiecek (Clemson University, US)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Jutka Gasiorowski zu administrativen Fragen

Marsha Kleinbauer zu wissenschaftlichen Fragen

Motivation

Multiobjective optimization (MO), a discipline within systems science that provides models, theories, and methodologies to address decision-making problems under conflicting objectives, has a myriad of applications in all areas of human activity, ranging from business and management to engineering. The proposed seminar is motivated by the desire to continue to make MO useful to society as it faces complex decision-making problems and experiences limited resources for decision making. Of particular interest are processes that evolve competitively in environments with scarce resources and lead to decision problems that are characterized by multiple, incommensurate, and conflicting objectives, and engage multiple decision-makers.

The Dagstuhl Seminar will focus on three major types of resource limitations: methodological (e.g., number of solution evaluations), technical (e.g., computation time, energy consumption), and human-related (e.g., decision maker availability and responsiveness). The effect of these limitations on optimization and decision-making quality, as well as methods to quantify and mitigate this influence, will be of particular interest. These methods include reduction and decomposition of optimization and decision-making models; representation of solution sets; different types of optimization approaches such as coordination-based, Bayesian, and multi-stage; preference acquisition; and benchmarking of algorithms. Applications within engineering-design optimization, simulation optimization, and experiment-based optimization will serve as motivation.

Following on the tradition of earlier Dagstuhl Seminars on multiobjective optimization, the seminar will be a platform for experts in two main research communities - Evolutionary MO (EMO) and Multiobjective Decision Making (MCDM) - to propose and discuss novel ideas related to modeling, theory, and applications of MO under competitive conditions and limited budgets.

Motivation text license
  Creative Commons BY 4.0
  Richard Allmendinger, Carlos M. Fonseca, Serpil Sayin, and Margaret M. Wiecek

Dagstuhl-Seminar Series

Classification

  • Machine Learning
  • Neural And Evolutionary Computing
  • Systems And Control

Keywords

  • Decision making
  • Expensive optimization
  • Few-shot learning
  • Evolutionary algorithms
  • Simulation optimization

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.