4 research outputs found
Collaborative Multi-Objective Optimization for Distributed Design of Complex Products
Multidisciplinary design optimization problems with competing
objectives that involve several interacting components can be called
complex systems. Nowadays, it is common to partition the optimization
problem of a complex system into smaller subsystems,
each with a subproblem, in part because it is too difficult to deal
with the problem all-at-once. Such an approach is suitable for large
organisations where each subsystem can have its own (specialised)
design team. However, this requires a design process that facilitates
collaboration, and decision making, in an environment where
teams may exchange limited information about their own designs,
and also where the design teams work at different rates, have different
time schedules, and are normally not co-located. A multiobjective
optimization methodology to address these features is
described. Subsystems exchange information about their own optimal
solutions on a peer-to-peer basis, and the methodology enables
convergence to a set of optimal solutions that satisfy the overall
system. This is demonstrated on an example problem where the
methodology is shown to perform as well as the ideal, but “unrealistic”
approach, that treats the optimization problem all-at-once
Multiobjective optimization for interwoven systems
In practical situations, complex systems are often composed of subsystems or subproblems with single or multiple objectives. These subsystems focus on different aspects of the overall system, but they often have strong interactions with each other and they are usually not sequentially ordered or obviously decomposable. Thus, the individual solutions of subproblems do not generally induce a solution for the overall system. Here, we strive to identify "re-composition architectures" of such "interwoven" systems. Our intention is to connect the subsystems adequately, analyze the resulting performance, model/solve the overall system, and improve the overall solution instead of just solving each subsystem separately. We review recent developments in this field and discuss modeling and solution paradigms in a general and unified framework using the example of an interwoven system consisting of two interacting subsystems
Understanding Complexity in Multiobjective Optimization
This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity