~~NOTOC~~
====== Optimization ======



===== Current and Former Projects =====


=== Multiobjective Control based on Averaging Hausdorff Measure ===
{{http://www.daad.de/static/pics/logo_daad_01.gif?250:nolink|}} 
This project in cooperation with [[http://www.cinvestav.mx/Default.aspx?alias=www.cinvestav.mx/ingles&language=en-US|CINVESTAV-IPN (Mexico City)]] is partially funded by DAAD and CONACYT. We aim at developing methods, based on the so-called averaging Hausdorff measure, that enable multiobjective algorithms to approximate Pareto fronts with evenly spaced points which is needed for a smooth control of systems.



=== Structure Optimization of Energy Supply Systems ===

{{ :rudolph:RTEmagicC_Werkteil_Knapsack.jpg?230:nolink|}}
{{:rudolph:sOptimo-logo.png?150:nolink |}}

Energy efficiency is a hot topic, because reducing consumption reduces cost
and pollution at the same time. When not only one plant, but a system of
plants as found in a whole industrial park is considered, savings potential
is increased enormously, but so also is the complexity.

Energy consumption of every single plant is not static
but depends on factors as environmental temperature, product demand,
and energy cost structure, but is also influenced by 'soft' factors
as preference decisions done by humans. On the larger scale (the picture
on the right shows the chemical park Knapsack near Cologne), the multitude
of demands has to be served and ideally, implemented solutions shall
cope well with changing demands (new plants, production change leading
to energy flow structure changes). 
We employ evolutionary algorithms at different levels of this highly
complex optimization problem.


This project was funded by BMWi and coordinated by the 
[[http://www.ltt.rwth-aachen.de/en/research/energy_systems_engineering/energy_system_technique/project/Strukturoptimierung_von_Energiev-4/|Chair of Technical Thermodynamics at the RWTH Aachen]].



{{ :rudolph:blechwanne.png?230:nolink|}}
=== Prediction of Forming Processes by Gaussian Process Models ===
{{:rudolph:logo_zim_screen.jpg?150:nolink |}}
This project in cooperation with [[http://www.simuform.de/|SimuForm GmbH]] and [[http://www3.fh-swf.de/institute/cv-ci/cvci_en.htm|South Westphalia University of Applied Sciences]] was funded by BMWi. Its goal was to predict the outcomes of forming processes, e.g. the thickness of the sheet metal, with Gaussian Process Models (GPM), also known as Kriging. 


===== Methods/Topics =====

  * Multi-objective optimization using [[:rudolph:hypervolume:|hypervolume-based]] approaches
  * Metamodel-assisted optimization, mainly using [[:rudolph:kriging:|Kriging]]
  * [[rudolph:multimodal:start|Multilocal/multimodal optimization]]


A large portion of our group's research deals with black-box optimization. We mainly apply [[:rudolph:ea|evolutionary algorithms]] (EA) to these problems. EAs have several desireable properties:

  * direct search method, no need for gradients
  * can handle single-objective and multi-objective problems
  * can cope with noisy and dynamic environments
  * ideal for parallelization


===== Conferences =====

  * [[/people/rudolph/research/conflist.jsp|Conference calendar]]
  * [[:rudolph:ppsn|Parallel Problem Solving from Nature]]