January 11 – 16 , 2015, Dagstuhl Seminar 15031
Understanding Complexity in Multiobjective Optimization
1 / 3 >
For support, please contact
Understanding complexity in multiobjective optimization is of central importance for the two communities, MCDM and EMO, and several related disciplines. It enables us to wield existing methodologies with greater knowledge, control and effect, and should, more importantly, provide the foundations and impetus for the development of new, principled methods, in this area.
We believe that a strong route to further progress in multiobjective optimization is a determination to understand more about the various ways that complexity manifests itself in multiobjective optimization. We observe that in several fields, ranging from engineering to medicine to economics to homeland security, real-world problems are very often characterized by a high degree of complexity deriving from the presence of many competitive objectives to be optimized, many stakeholders expressing conflicting interests and the presence of many technical parameters being unstable in time and for which we have imperfect knowledge. These very complex problems require a specific methodology, mainly based on multiobjective optimization, that, using high computational capacities, takes into account robustness concerns and allows an effective participation of the several stakeholders in the decision process.
The seminar took place January 11th--16th 2015. The main goals of the seminar were the exploration and elucidation of complexity in three fundamental domains:
Focus 1: Complexity in preference
This topic is mainly concerned with elicitation, representation and exploitation of the preference of one or more users, for example: discovering and building preferences that are dynamic and unstable, group preference, complex structure of criteria,non-standard preferences, learning in multiobjective optimization.
Focus 2: Complexity in optimization
This topic is mainly concerned with the generation of alternative candidate solutions, given some set of objective functions and feasible space. The following topics are examples for the wide range of issues in this context: high-dimensional problems, complex optimization problems, simulation-based optimization and expensive functions, uncertainty and robustness, interrelating decision and objective space information.
Focus 3: Complexity in applications
An all-embracing goal is to achieve a better understanding of complexity in practical problems. Many fields in the Social Sciences, Economics, Engineering Sciences are relevant: E-government, Finance, Environmental Assessment, E-commerce, Public Policy Evaluation, Risk Management and Security issues are among the possible application areas.
During the seminar the program was updated on a daily basis to maintain flexibility in balancing time slots for talks, discussions, and working groups. The working groups were established on the first day in highly interactive fashion: at first each participant was requested to write her/his favorite topic on the black board, before a kind of collaborative clustering process was applied for forming the initial five working groups, some of them splitting into subgroups later. Participants were allowed to change working groups during the week, but the teams remained fairly stable throughout. Abstracts of the talks and extended abstracts of the working groups can be found in subsequent chapters of this report.
Further notable events during the week included: (i) a session devoted to discuss the results and the perspectives of this series of seminars after ten years of the first one, (ii) a hike within a time slot with worst weather conditions during the week, (iii) a presentation session allowing us to share details of upcoming events in our research community, and (iv) a wine and cheese party made possible by a donation of UCL's EPSRC Centre for Innovative Manufacturing in Emergent Macromolecular Therapies represented by Richard Allmendinger.
The outcomes of each of the working groups can be seen in the sequel. Extended versions of their findings will be submitted to a Special Issue on "Understanding Complexity in Multiobjective Optimization" in the Journal of Multi-Criteria Decision Analysis guest-edited by the organizers of this Dagstuhl seminar.
This seminar resulted in a very insightful, productive and enjoyable week. It has already led to first new results and formed new cooperation, research teams and topics. In general, the relations between the EMO and MCDM community were further strengthened after this seminar and we can expect that thanks to the seminar a greater and greater interaction will be developed in the next few years.
Acknowledgements. Many thanks to the Dagstuhl office and its helpful and patient staff; huge thanks to the organizers of the previous seminars in the series for setting us up for success; and thanks to all the participants, who worked hard and were amiable company all week. In the appendix, we also give special thanks to Salvatore Greco as he steps down from the organizer role.
Creative Commons BY 3.0 Unported license
Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and Günter Rudolph
Dagstuhl Seminar Series
- 18031: "Personalized Multiobjective Optimization: An Analytics Perspective" (2018)
- 12041: "Learning in Multiobjective Optimization" (2012)
- 09041: "Hybrid and Robust Approaches to Multiobjective Optimization" (2009)
- 06501: "Practical Approaches to Multi-Objective Optimization " (2006)
- 04461: "Practical Approaches to Multi-Objective Optimization" (2004)
- Modelling / Simulation
- Optimization / Scheduling
- Soft Computing / Evolutionary Algorithms
- Multi-criteria optimization
- Multiple criterion decision making
- Evolutionary multiobjective optimization
- Hybrid methods