BlueM.Opt: Difference between revisions

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[[Bild:EVO Box screenshot.png|thumb|Screenshot]]
[[Bild:EVO Box screenshot.png|thumb|Screenshot]]
==Description==
==Description==
BlueM.Opt is an optimization framework that allows an arbitrary simulation software to combine (only requirement: Input data and results are to be stored in ASCII format) with an optimization. The optimized parameters, objective functions and (optionally) boundary can be easily determined.
BlueM.Opt is an optimization framework that can be coupled with an arbitrary simulation software (only requirement: input data and results are to be stored in ASCII format). The optimization parameters, objective functions and (optionally) boundary conditions are defined in a flexible manner.


Optimization results are stored in a database.  
Optimization results are stored in a database.  


BlueM.Opt integrates a powerful function graph with the optimization results are displayed and can be analyzed.  
BlueM.Opt integrates a graphing feature for displaying the optimization progress and results. Optimization results can also be analyzed in detail.
 
Where possible, BlueM.Opt utilizes multithreading in order to evaluate multiple parameter sets simultaneously.  


List of available methods (optimization algorithms):
List of available methods (optimization algorithms):
* '''[[PES]]''': Parametric Evolution Strategy
* '''[[PES]]''': Parametric Evolution Strategy
* '''[[CES]]''': Combinatorial Evolution Strategy
* '''[[CES]]''': Combinatorial Evolution Strategy
* '''[[HYBRID]]''': Combination of PES and CES
* '''[[HYBRID]]''': Combination of PES and CES
* '''[[Hooke & Jeeves]]''': Hillclimbing Algorithm
* '''[[Hooke & Jeeves]]''': Hillclimbing Algorithm
* '''[[MetaEvo]]''': multi-criterion, hybrid optimization algorithm
* '''[[MetaEvo]]''': multicritera, hybrid optimization algorithm
* '''[[DDS]]''': Dynamically Dimensioned Search
* '''[[DDS]]''': Dynamically Dimensioned Search
another method is
another method is
* '''[[SensiPlot]]''': Sensitivity analysis (no optimization)
* '''[[SensiPlot]]''': Sensitivity analysis (no optimization)
* '''[[Traveling Salesman]]'''
List of currently implemented simulation models:
* '''[[BlueM.Sim]]'''
* '''[[SMUSI]]'''
* '''[[S:CAN]]'''
* '''[[SWMM]]'''
* '''[[Test problems]]'''


==Downloads==
==Downloads==

Revision as of 06:01, 30 June 2009

EVO.png BlueM.Opt | Download | Usage | Development

Simulation-based optimization
Screenshot

Description

BlueM.Opt is an optimization framework that can be coupled with an arbitrary simulation software (only requirement: input data and results are to be stored in ASCII format). The optimization parameters, objective functions and (optionally) boundary conditions are defined in a flexible manner.

Optimization results are stored in a database.

BlueM.Opt integrates a graphing feature for displaying the optimization progress and results. Optimization results can also be analyzed in detail.

Where possible, BlueM.Opt utilizes multithreading in order to evaluate multiple parameter sets simultaneously.

List of available methods (optimization algorithms):

  • PES: Parametric Evolution Strategy
  • CES: Combinatorial Evolution Strategy
  • HYBRID: Combination of PES and CES
  • Hooke & Jeeves: Hillclimbing Algorithm
  • MetaEvo: multicritera, hybrid optimization algorithm
  • DDS: Dynamically Dimensioned Search

another method is

List of currently implemented simulation models:

Downloads

Application

Development

Internal