Method and apparatus for adaptively determining weight factors within the context of an objective function
First Claim
1. A computer implemented method of adaptively adjusting an objective function providing a figure of merit for a proposed solution to a constraint problem with a processing device, comprising:
- determining a value for each penalty component of the objective function, each penalty component including a penalty term multiplied by a weight factor from a first set of weight factors, the objective function including at least two penalty components;
determining a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value; and
changing weight factors in a second set of weight factors that correspond to the determined worst penalty component and at least one other penalty component to create an adapted objective function such that the worst penalty component has increased emphasis.
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Abstract
The method and apparatus adaptively determine weighting factors within the context of an objective function for handling optimality conditions and constraints within an optimization search. The objective function is defined as a sum of credit and penalty components. The credit components represent the optimality conditions for the problem. The penalty components represent the constraint violations for the problem. Initially, each component is made up of a weight multiplied by a mathematical expression, called a term, that quantifies either an optimality condition or a constraint violation. The set of credit and penalty weights are adaptively determined based on the progress of an optimization search. Both static and dynamic representations of the modified objective function are used to perform the adaption.
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Citations
30 Claims
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1. A computer implemented method of adaptively adjusting an objective function providing a figure of merit for a proposed solution to a constraint problem with a processing device, comprising:
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determining a value for each penalty component of the objective function, each penalty component including a penalty term multiplied by a weight factor from a first set of weight factors, the objective function including at least two penalty components; determining a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value; and changing weight factors in a second set of weight factors that correspond to the determined worst penalty component and at least one other penalty component to create an adapted objective function such that the worst penalty component has increased emphasis. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer implemented method of adaptively adjusting an objective function providing a figure of merit for a proposed solution to a constraint problem with a processing device, comprising:
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first determining a value for each penalty component of the objective function, each component of the objective function including a term multiplied by a weight factor from a first set of weight factors, the objective function including at least one penalty component and at least one credit component; second determining whether a constraint violation exists based on output from the determining a value; if the second determining determines that a constraint violation does not exist, first changing a weight factor in a second set of weight factors that corresponds to the credit component; and if the second determining determines that a constraint violation does exist, third determining a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value, and second changing weight factors in the second set of weight factors corresponding to the determined worst penalty component and at least one other component in the second set of weight factors to create an adapted objective function such that the worst penalty component has increased emphasis. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A computer readable medium, comprising:
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a code arrangement instructing a processor to determine a value for each penalty component of an objective function, each penalty component including a penalty term multiplied by a weight factor from a first set of weight factors, the objective function including at least two penalty components; a code arrangement instructing a processor to determine a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value; and a code arrangement instructing a processor to change weight factors in a second set of weight factors that correspond to the determined worst penalty component and at least one other penalty component to create an adapted objective function such that the worst penalty component has increased emphasis.
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26. A computer readable medium, comprising:
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a code arrangement instructing a processor to first determine a value for each penalty component of an objective function, each component of the objective function including a term multiplied by a weight factor from a first set of weight factors, the objective function including at least one penalty component and at least one credit component; a code arrangement instructing a processor to second determine whether a constraint violation exists based on the determined penalty component values; a code arrangement instructing a processor to first change a weight factor in a second set of weight factors that corresponds to the credit component if the second determining determines that a constraint violation does not exist and; a code arrangement instructing a processor to, if the second determining determines that a constraint violation does exist, third determine a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value, and second change weight factors in the second set of weight factors corresponding to the determined worst penalty component and at least one other component in the second set of weight factors to create an adapted objective function such that the worst penalty component has increased emphasis.
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27. A computer implemented method of adaptively adjusting an objective function providing a figure of merit for a proposed solution to a nuclear constraint problem with a processing device, comprising:
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determining a value for each penalty component of the objective function, each penalty component including a penalty term multiplied by a weight factor from a first set of weight factors, the objective function including at least two penalty components; determining a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value; and changing weight factors in a second set of weight factors that correspond to the determined worst penalty component and at least one other penalty component to create an adapted objective function such that the worst penalty component has increased emphasis. - View Dependent Claims (28)
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29. A computer implemented method of adaptively adjusting an objective function providing a figure of merit for a proposed solution to a nuclear reactor constraint problem with a processing device, comprising:
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first determining a value for each penalty component of the objective function, each component of the objective function including a term multiplied by a weight factor from a first set of weight factors, the objective function including at least one penalty component and at least one credit component; second determining whether a constraint violation exists based on output from the determining a value; if the second determining determines that a constraint violation does not exist, first changing a weight factor in a second set of weight factors that corresponds to the credit component; and if the second determining determines that a constraint violation does exist, third determining a worst penalty component using the determined penalty component values, wherein the worst penalty component is one of the penalty components with the greatest value, and second changing weight factors in the second set of weight factors corresponding to the determined worst penalty component and at least one other component in the second set of weight factors to create an adapted objective function such that the worst penalty component has increased emphasis. - View Dependent Claims (30)
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Specification