Intelligent space tube optimizer
First Claim
1. A computer-implemented apparatus for simulation/optimization, comprising:
- a simulation/optimization computational engine;
a surrogate simulation engine;
a memory associated with said simulation/optimization computational engine and with said surrogate simulation engine for storing computation results;
a data storage means for storing said computation results in said memory;
an optimization stopping criterion;
an objective function;
a stopping criteria;
a constraint definition;
a solution strategy;
a definition of a multi-dimensional space tube containing a subspace;
(a) said simulation/optimization computational engine generating simulations within said subspace of said multi-dimensional space tube;
(b) performing optimization using said surrogate simulation engine;
(c) analyzing optimization strategy computed in step (b) concerning valuation of said objective function for said optimization strategy computed in step (b);
(d) analyzing optimization strategy computed in step (b) concerning valuation of said constraint definition for said optimization strategy computed in step (b);
(e) updating said multi-dimensional space tube and subspace by computing a modified multi-dimensional space tube and subspace;
(f) computing an updated solution strategy from optimization strategy computed in step (b);
(g) saving said updated solution strategy and said updated space tube and subspace to said memory;
(h) evaluating said stopping criteria; and
iterating sequence (a) through (h) using said updated solution strategy and updated space tube and subspace as inputs to said computational engine until said stopping criterion is met.
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Abstract
This disclosure presents the Intelligent Space Tube Optimization (ISTO) method for developing computationally optimal designs or strategies. ISTO is especially valuable if predicting system response to the design or strategy is computationally intensive, and many predictions are needed in the optimization process. ISTO creates adaptively evolving multi-dimensional decision space tube(s), develops or trains surrogate simulators for the space about the tube(s), performs optimization about the tube(s) using primarily the surrogate simulators and selected optimizer(s), and then can revert to an original simulator for efficient final optimizations. A space tube consists of overlapping multi-dimensional subspaces, and lengthens in the direction of the optimal solution. The space tube can shrink or expand to aid convergence and escape from local optima. ISTO can employ any appropriate type of surrogate simulator and can employ any type of optimizer. ISTO includes a multiple cycling approach. One ISTO cycle involves: (i) defining the multi-dimensional space tube; (ii) generating strategies about the space tube'"'"'s subspace; (iii) simulating system response to the strategies using an original simulator; (iv) developing or training surrogate simulators, such as regression equations or ANNs; (v) performing optimization about the subspace, primarily using the substitute simulators; (vi) analyzing the optimal strategy; and (vii) evaluating whether space tube radius (radii) modification is required. Based on optimization performance or to escape from a locally optimal solution, the ISTO automatically adjusts the space tube dimensions and location. ISTO cycling terminates per stopping criterion. After cycling terminates, ISTO can proceed to optimize while employing an original simulator, rather than the surrogate. This feature is useful because when optimization problem constraints become extremely tight, predictive accuracy becomes increasingly important.
20 Citations
34 Claims
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1. A computer-implemented apparatus for simulation/optimization, comprising:
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a simulation/optimization computational engine;
a surrogate simulation engine;
a memory associated with said simulation/optimization computational engine and with said surrogate simulation engine for storing computation results;
a data storage means for storing said computation results in said memory;
an optimization stopping criterion;
an objective function;
a stopping criteria;
a constraint definition;
a solution strategy;
a definition of a multi-dimensional space tube containing a subspace;
(a) said simulation/optimization computational engine generating simulations within said subspace of said multi-dimensional space tube;
(b) performing optimization using said surrogate simulation engine;
(c) analyzing optimization strategy computed in step (b) concerning valuation of said objective function for said optimization strategy computed in step (b);
(d) analyzing optimization strategy computed in step (b) concerning valuation of said constraint definition for said optimization strategy computed in step (b);
(e) updating said multi-dimensional space tube and subspace by computing a modified multi-dimensional space tube and subspace;
(f) computing an updated solution strategy from optimization strategy computed in step (b);
(g) saving said updated solution strategy and said updated space tube and subspace to said memory;
(h) evaluating said stopping criteria; and
iterating sequence (a) through (h) using said updated solution strategy and updated space tube and subspace as inputs to said computational engine until said stopping criterion is met. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer-implemented apparatus for simulation/optimization, comprising:
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a simulation/optimization computational engine;
a surrogate simulation engine;
a memory associated with said simulation/optimization computational engine and with said surrogate simulation engine for storing computation results;
a data storage means for storing said computation results in said memory;
an optimization stopping criterion;
an objective function;
a stopping criteria;
a constraint definition;
a solution strategy;
a definition of a multi-dimensional space tube containing a subspace;
(a) said simulation/optimization computational engine generating simulations within said subspace of said multi-dimensional space tube;
(b) training said surrogate simulation engine using said simulations generated in step (a);
(c) performing optimization using said trained surrogate simulation engine;
(d) analyzing optimization strategy computed in step (c) concerning valuation of said objective function for said optimization strategy computed in step (c);
(e) analyzing optimization strategy computed in step (c) concerning valuation of said constraint definition for said optimization strategy computed in step (c);
(f) updating said multi-dimensional space tube and subspace by computing a modified multi-dimensional space tube and subspace;
(g) computing an updated solution strategy from optimization strategy computed in step (c);
(h) saving said updated solution strategy and said updated space tube and subspace to said memory;
(i) evaluating said stopping criteria; and
iterating sequence (a) through (i) using said updated solution strategy and updated space tube and subspace as inputs to said computational engine until said stopping criterion is met. - View Dependent Claims (16, 17, 18, 19, 20)
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21. A method for simulation/optimization, comprising:
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a simulation computational process;
a surrogate simulation process;
an optimization stopping criterion;
an objective function;
a stopping criteria;
a constraint definition;
a solution strategy;
a definition of a multi-dimensional space tube containing a subspace;
(a) said simulation computation process generating simulations within said subspace of said multi-dimensional space tube;
(b) performing optimization using said surrogate simulation process;
(c) analyzing optimization strategy computed in step (b) concerning valuation of said objective function for said optimization strategy computed in step (b);
(d) analyzing optimization strategy computed in step (b) concerning valuation of said constraint definition for said optimization strategy computed in step (b);
(e) updating said multi-dimensional space tube and subspace by computing a modified multi-dimensional space tube and subspace;
(f) computing an updated solution strategy from optimization strategy computed in step (b);
(g) evaluating said stopping criteria; and
iterating sequence (a) through (g) using said updated solution strategy and updated space tube and subspace as inputs to said computational process until said stopping criterion is met. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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Specification
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- Resources
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Current AssigneeUtah State University
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Original AssigneeUtah State University
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InventorsPeralta, Richard, Kalwij, Ineke
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Application NumberUS11/650,173Publication NumberTime in Patent OfficeDaysField of SearchUS Class Current1/1CPC Class CodesG06N 3/02 Neural networks
