=== Apologies for cross-posting ===
The Special Issue on "Understanding Complexity in Multiobjective
Optimization"
Guest Editors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter
Rudolph
has extended the submission deadline to _31 October 2015_
Many thanks and best regards,
Joshua
--
Details:
Special Issue on "Understanding Complexity in Multiobjective Optimization"
Guest Editors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter
Rudolph
This special issue of JMCDA is devoted to further strengthen the links
between the Evolutionary
Multiobjective Optimization (EMO) and Multiple Criteria Decision Making
(MCDM) communities, and to
advance our understanding of different aspects concerning complexity in
multiobjective optimization.
The need for a better understanding of complexity is pressing and timely as
recent work has sometimes
shown opposing views regarding how problems scale and grow in difficulty,
and their inherent
challenges. On the one hand, we know that multiobjective optimization
problems are complex problems
by their very nature. For example optimization problems that are easy to
solve in the single objective case are often
intractable and highly complex already in the biobjective case, and
further fundamental limitations in multiobjective optimization are apparent
as we scale up to many objectives.
On the other hand, a multiobjective perspective can in a sense also help
reduce complexity. For example,
it often leads to a better understanding of a problem and hence supports
the decision making process.
And adding objectives to a problem does not always make it harder, because
decomposing it can
reduce the presence of local optima, for example. From the MCDM point of
view, we observe that
there is an intrinsic complexity in the process of
understanding the optimization problem and building preferences on the
solutions proposed by the
multiobjective optimization. At the beginning of the decision process the
Decision Maker (DM) has a
rather vague idea of the decision problem at hand and, consequently, also
the preferences are
incomplete, approximate, uncertain or fuzzy.
Taking into account the above remarks, complexity in multiobjective
optimization, as the main theme of the special
issue, will be focused around three topics:
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.
These three topics will provide a strong basis for progress in both the
theory and practice of handling
complexity in multiobjective optimization in all its guises.
Submissions are now invited for this special issue, which will be subject
to the standard review processes
of the journal. Contributions should be submitted online at
http://mc.manuscriptcentral.com/mcda,
making sure that the box asking whether the submission is for special issue
is checked, and that
"understanding complexity in multiobjective optimization" is entered into
the appropriate field.
The final date for submissions is XX31XXJulyXX now 31 October 2015.
--
Joshua Knowles
Professor of Natural Computation
http://www.cs.bham.ac.uk/~jdk/
School of Computer Science, University of Birmingham, UK
and Honorary Professor, MBS, University of Manchester, UK
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************
Posts
