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Many-Criteria Optimization and Decision Analysis

State-of-the-Art, Present Challenges, and Future Perspectives

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  • © 2023

Overview

  • Authored by leading experts in the field
  • Identifies and discusses the key research topics in many-criteria optimization
  • Contributes to evolutionary computing, operations research, multiobjective optimization, and decision science

Part of the book series: Natural Computing Series (NCS)

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Table of contents (13 chapters)

  1. Key Research Topics

  2. Emerging Topics

Keywords

About this book

This book presents the state-of-the-art, current challenges, and future perspectives for the field of many-criteria optimization and decision analysis. The field recognizes that real-life problems often involve trying to balance a multiplicity of considerations simultaneously – such as performance, cost, risk, sustainability, and quality. The field develops theory, methods and tools that can support decision makers in finding appropriate solutions when faced with many (typically more than three) such criteria at the same time. 

The book consists of two parts: key research topics, and emerging topics. Part I begins with a general introduction to many-criteria optimization, perspectives from research leaders in real-world problems, and a contemporary survey of the attributes of problems of this kind. This part continues with chapters on fundamental aspects of many-criteria optimization, namely on order relations, quality measures, benchmarking, visualization, and theoretical considerations. Part II offers more specialized chapters on correlated objectives, heterogeneous objectives, Bayesian optimization, and game theory.

Written by leading experts across the field of many-criteria optimization, this book will be an essential resource for researchers in the fields of evolutionary computing, operations research, multiobjective optimization, and decision science.

Editors and Affiliations

  • Inria Saclay-Île-de-France and CMAP, École Polytechnique, Palaiseau, France

    Dimo Brockhoff

  • Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  • Informatik und Ingenieurwissenschaften, TH Köln, Gummersbach, Germany

    Boris Naujoks

  • Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Robin Purshouse

About the editors

Dimo Brockhoff is a researcher at Inria, France, and member of the Randomized Optimization team that is co-located with Ecole Polytechnique, IP Paris. He studied computer science in Dortmund, Germany and received a PhD from ETH Zurich, Switzerland for his work “Many-Objective Optimization and Hypervolume Based Search”. His research interests are focused on multiobjective optimization and benchmarking.

Michael Emmerich is an Associate Professor at Leiden University and has recently also been an international research fellow at the University of Jyväskylä, Finland. At Leiden University he leads since 2011 the Multi-objective Optimization and Decision Analysis (MODA) research group. He received his doctorate in 2005 on the topic "Gaussian Process Models in Multiobjective Optimization" from TU Dortmund, Germany (promotor: Hans-Paul Schwefel). He chaired four leading international optimization conferences and five Lorentz center workshops. He published more than 200articles on multiobjective optimization, mainly indicator-based algorithms and Bayesian multiobjective optimization, and applications such as architectural design, logistics, and computational chemistry.

Boris Naujoks is a professor of Applied Mathematics at TH Köln - Cologne University of Applied Sciences (THK). He joined THK directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Now, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of THK. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, benchmarking, and the (industrial) applicability of such techniques.

Robin Purshouse is Professor of Decision Sciences at the University of Sheffield, UK. He received his PhD in Control Systems from the University of Sheffield in 2004 for his thesis “On the Evolutionary Optimisation of Many Objectives”, which was one of the earliest works on the topic of many-criteria optimization. He was General Chair of the Seventh International Conference on Evolutionary Multi-Criterion Optimization held in Sheffield in 2013. His research interests in multi-objective optimization are inspired by real-world problems and include robust optimization, surrogate-based optimization, and multi-disciplinary optimization.

Bibliographic Information

  • Book Title: Many-Criteria Optimization and Decision Analysis

  • Book Subtitle: State-of-the-Art, Present Challenges, and Future Perspectives

  • Editors: Dimo Brockhoff, Michael Emmerich, Boris Naujoks, Robin Purshouse

  • Series Title: Natural Computing Series

  • DOI: https://doi.org/10.1007/978-3-031-25263-1

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-25262-4Published: 29 July 2023

  • Softcover ISBN: 978-3-031-25265-5Due: 29 August 2023

  • eBook ISBN: 978-3-031-25263-1Published: 28 July 2023

  • Series ISSN: 1619-7127

  • Series E-ISSN: 2627-6461

  • Edition Number: 1

  • Number of Pages: XIII, 360

  • Number of Illustrations: 11 b/w illustrations, 63 illustrations in colour

  • Topics: Theory of Computation, Operations Research/Decision Theory

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