Systems and methods for multi-objective optimizations with decision variable perturbations
First Claim
1. A method, comprising:
- determining, by one or more processors of a multi-objective heuristic system, a first chromosome and a second chromosome associated with a multi-objective optimization;
generating, by the one or more processors, a third chromosome by performing a genetic operation on the first chromosome and the second chromosome;
determining, by the one or more processors and based at least in part on one or more constraint models, that the third chromosome is infeasible, wherein the one or more constraint models provide an indication of a first gene of a constraint, a second gene that is a source of the constraints, and a magnitude of the constraint;
perturbing, by the one or more processors and based at least in part on a decision variable perturbation model, at least one of the first gene or the second gene;
perturbing, by the one or more processors and based at least in part on the perturbation of the one of the first gene and the second gene, the other of the first gene or the second gene to generate a perturbed third chromosome;
determining, by the one or more processors, that the perturbed third chromosome is feasible; and
providing, by the one or more processors, the perturbed third chromosome as a solution to the multi-objective optimization.
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Abstract
Systems and methods are provided for providing an optimized solution to a multi-objective problem. Potential solutions may be generated from parent solutions to be evaluated according to multiple objectives of the multi-objective problem. If the potential solutions are infeasible, the potential solutions may be perturbed according to a perturbation model to bring the potential solution to feasibility, or at least a reduced level of constraints. The perturbation models may include a weight vector that indicates the amount of perturbation, such as in a forward and/or reverse direction, of decision variables of the potential solutions. In some cases, the perturbation models may be predetermined. In other cases, the perturbation models may be learned, such as based on training constraint data. Additionally, potential solutions may be generated in a secondary optimization where a constraint based optimization may be performed to drive to generating a feasible solution for further evaluation according to objective values.
56 Citations
20 Claims
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1. A method, comprising:
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determining, by one or more processors of a multi-objective heuristic system, a first chromosome and a second chromosome associated with a multi-objective optimization; generating, by the one or more processors, a third chromosome by performing a genetic operation on the first chromosome and the second chromosome; determining, by the one or more processors and based at least in part on one or more constraint models, that the third chromosome is infeasible, wherein the one or more constraint models provide an indication of a first gene of a constraint, a second gene that is a source of the constraints, and a magnitude of the constraint; perturbing, by the one or more processors and based at least in part on a decision variable perturbation model, at least one of the first gene or the second gene; perturbing, by the one or more processors and based at least in part on the perturbation of the one of the first gene and the second gene, the other of the first gene or the second gene to generate a perturbed third chromosome; determining, by the one or more processors, that the perturbed third chromosome is feasible; and providing, by the one or more processors, the perturbed third chromosome as a solution to the multi-objective optimization. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system, comprising:
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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; determine a first chromosome and a second chromosome associated with a multi-objective optimization; generate a third chromosome by performing a genetic operation on the first chromosome and the second chromosome; determine, based at least in part on one or more constraint models, that the third chromosome is infeasible, wherein the one or more constraint models provide an indication of a first gene of a constraint, a second gene that is a source of the constraints, and a magnitude of the constraint; perturb, based at least in part on a decision variable perturbation model, at least one of the first gene or the second gene; perturb, by the one or more processors and based at least in part on the perturbation of the one of the first gene and the second gene, the other of the first gene or the second gene to generate a perturbed third chromosome; determine that the perturbed third chromosome is feasible; and provide the perturbed third chromosome as a solution to the multi-objective optimization. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification