Strategy parameter adaptation in evolution strategies

• US 7,243,056 B2
• Filed: 02/21/2002
• Issued: 07/10/2007
• Est. Priority Date: 02/26/2001
• Status: Expired due to Fees

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Sendhoff, Bernhard, Olhofer, Markus
• Primary Examiner(s): Rodriguez; Paul
• Assistant Examiner(s): SHARON, AYAL I
• Application Number:
  US10/080,742
• Publication Number:
  US 20020165703A1
• Time in Patent Office: 1,965 Days
• Field of Search: 703/2, 703/7, 703/6, 706/15, 706/26
• US Class Current: 703/7
• CPC Class Codes: G06F 2111/06 Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

The present invention relates to a optimization method based on an evolution strategy according to which a model/structure/shape/design to be optimized is described by parameter sets comprising object parameters. The object parameters are mutated to create offsprings of the parameter set. The quality of the offsprings is evaluated. The parameter set furthermore comprises at least one strategy parameter representing the step-size of the mutation (f.e. the variance of the normal distribution) of associated object parameters. The number of object parameters as well as the number of associated strategy parameters can be adapted during the optimization process. The value of newly inserted strategy parameters can be estimated based on the information of correlated object parameters.

Claims

1. A computer based method of optimizing one of a model, structure, shape or design representing an aerodynamic structure or a hydrodynamic structure based on an evolution strategy, comprising:

describing one of the model, structure, shape or design representing an aerodynamic structure or a hydrodynamic structure to be optimized using a parameter set comprising a first number of object parameters;

creating offsprings of the parameter set by modifying the object parameters, wherein said modifying includes at least one of mutating the object parameters and recombining the object parameters;

evaluating quality of the offsprings;

wherein the parameter set comprises at least one strategy parameter representing a step-size of the mutation of associated object parameters;

modifying said first number of the object parameters to a second number of object parameters and modifying a first number of associated strategy parameters to a second number of associated strategy parameters during optimization to optimize one of said model, structure, shape or design; and

storing one of said optimized model, optimized structure, optimized shape or optimized design in a computer storage.

2. The optimization method of claim 1 further comprising altering the object parameters and the strategy parameters, wherein said altering includes at least one of selectively inserting or removing an object parameter and a strategy parameter.

3. The optimization method of claim 2, further comprising estimating a value of a newly inserted strategy parameter based on information of strategy parameters associated with correlated object parameters.

4. The optimization method of claim 1, further comprising estimating a value of a newly inserted strategy parameter based on information of strategy parameters associated with correlated object parameters.

5. The optimization method of claim 1, further comprising determining a position of altering an object parameter and an associated strategy parameter using a random function.

6. The optimization method of claim 5, further comprising determining a time of altering the object parameter and the associated strategy parameter using a random function.

7. The optimization method of claim 1, further comprising determining a time of altering an object parameter and an associated strategy parameter using a random function.

8. The optimization method of claim 1, further comprising determining a position of altering an object parameter and an associated strategy parameter by progress of the evolutionary optimization.

9. The optimization method of claim 8, further comprising determining a time of said altering of said object parameter and the associated strategy parameter by the progress of the evolutionary optimization.

10. The optimization method of claim 8, further comprising determining a time of said altering of said object parameter and the associated strategy parameter by the progress of the evolutionary optimization.

11. The optimization method of claim 1, wherein the mutating of the object parameters does not directly influence the result of the evaluating step.

19. The method of claim 1, wherein the model, structure, shape or design representing an aerodynamic structure or a hydrodynamic structure comprises one of:

an airfoil;

a spline coded structure;

a turbine blade for a gas turbine;

an aerodynamic structure;

or

a hydrodynamic structure.

20. A computer program stored in a computer readable medium for performing the method of claim 1.

12. A computer based method of optimizing one of a model, structure, shape or design representing an aerodynamic structure or a hydrodynamic structure based on an evolution strategy, comprising:

describing one of the model, structure, shape or design representing an aerodynamic structure or a hydrodynamic structure to be optimized using a parameter set comprising a first number of object parameters;

creating offsprings of the parameter set by modifying the first number of object parameters to a second number of object parameters and modifying a structure of the parameter set, the structure of the parameter set defined by a number and position of the object parameters and strategy parameters; and

evaluating quality of the offsprings;

wherein the parameter set comprises at least one strategy parameter representing a step-size of the modification of associated object parameters; and

storing one of said optimized model, optimized structure, optimized shape or optimized design in a computer storage.

13. The optimization method of claim 12, wherein said step-size of the modification is a variance of a normal distribution.

14. The optimization method of claim 12, wherein said one of the model, structure, shape, or design is described using a spline.

15. The optimization method of claim 14, wherein the object parameters comprise control points and knot points, the method further comprising adapting a knot vector by inserting new control points and strategy parameters.

16. The optimization method of claim 15, further comprising estimating values of newly inserted strategy parameters based upon values of strategy parameters of neighboring control points.

21. A computer program stored in a computer readable medium for performing the method of claim 12.

17. A computer based method for optimizing a spline coded structure representing an aerodynamic structure or a hydrodynamic structure based on an evolution strategy, comprising:

describing the spline coded structure to be optimized using a parameter set comprising a first number of object parameters representing control points and knot points and at least one strategy parameter representing a step-size of a mutation of associated object parameters;

mutating the object parameters and strategy parameters to create offsprings of the set, comprising;

determining a control point insertion,

inserting the control point in the parameter set,

inserting an additional strategy parameter for the inserted control point,

determining the knot points modified by the insertion of the control point,

determining a weighted averaging of strategy parameter values of modified control points, and

assigning the weighted average value as a value of the inserted strategy parameter;

evaluating quality of the offsprings; and

storing the mutated object parameters and strategy parameters in a computer memory.

18. The method of claim 17, wherein said step-size of the mutation is a variance of a normal distribution.

22. A computer program stored in a computer readable medium for performing the method of claim 17.