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US 10,311,358 B2Granted
Systems and methods for multi-objective evolutionary algorithms with category discovery
AssigneeThe Aerospace Corporation
InventorTimothy Guy Thompson
Life of this patent
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Abstract
Systems and methods are provided to engage in multi-tiered optimization where there may be a first multi-objective optimization and a second constraint optimization. The multi-objective optimization may be used to drive to one or more goals of the optimization problem. The constraint optimization or minimization may be used to drive towards a reduced and/or no constraint situation where the solution to the overall problem is feasible or near-feasible.
Claims20
A method, comprising:
- identifying, by one or more processors, a plurality of items, including a first item and a second item;
- identifying, by the one or more processors, a plurality of categories, including a first category and a second category;
- providing, by the one or more processors, a first allocation of items by allocating the first item to the first category and the second item to the second category;
- identifying, by the one or more processors, one or more constraint models;
- determining, by the one or more processors and based at least in part on the one or more constraint models, that the first allocation of items is infeasible;
- providing, by the one or more processors and based at least in part on determining that the first allocation of items is infeasible, a second allocation of items by allocating the first item to a third category;
- determining, by the one or more processors and based at least in part on the one or more constraint models, that the second allocation is feasible;
- providing, by the one or more processors, the second allocation as a feasible solution to a multi-objective optimization problem;
- and
- performing, by the one or more processors, the multi-objective optimization problem using the second allocation.
7 more elements
A system, comprising:
- a memory that stores computer-executable instructions;
- at least one processor configured to access the memory, wherein the at least one processor is further configured to execute the computer-executable instructions to:
- identify a plurality of items, including a first item and a second item;
- identify a plurality of categories, including a first category and a second category;
- provide a first allocation of items by allocating the first item to the first category and the second item to the second category;
- identify one or more constraint models;
- determine, based at least in part on the one or more constraint models, that the first allocation of items is infeasible;
- provide, based at least in part on determining that the first allocation of items is infeasible, a second allocation of items by allocating the first item to a third category;
- determine, based at least in part on the one or more constraint models, that the second allocation is feasible;
- provide the second allocation as a feasible solution to a multi-objective optimization problem;
- and
- perform the multi-objective optimization problem using the second allocation.
9 more elements
Dependencies are inferred from the claim text.