Systems and methods for multi-objective evolutionary algorithms with soft constraints
First Claim
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1. A method, comprising:
- identifying, by one or more processors, a first chromosome, wherein the first chromosome includes a plurality of decision variables to be optimized in a multi-objective optimization;
identifying, by the one or more processors, a first constraint model corresponding to a first constraint and a second constraint model corresponding to a second constraint;
identifying, by the one or more processors, the first constraint as a hard-constraint and the second constraint as a soft constraint;
applying, by the one or more processors, the first constraint model to the first chromosome to derive a first constraint value;
applying, by the one or more processors, the second constraint model to the first chromosome to derive a second constraint value;
comparing, by the one or more processors, the first constraint value to a corresponding first threshold value to determine that the first chromosome does not violate the first constraint;
comparing, by the one or more processors, the second constraint value to a corresponding second threshold value to determine that the first chromosome does violate the second constraint;
indicating, by the one or more processors, that the first chromosome is soft infeasible, wherein a tag of soft infeasible indicates that only soft constraints have been violated;
determining, based at least in part on the second constraint value, that the first chromosome is to be crossed-over with a second chromosome;
generating a third chromosome by crossing over the first chromosome with the second chromosome;
determining, based at least in part on the first constraint model and the second constraint model, that the third chromosome is feasible; and
providing the third chromosome as an optimized solution to the multi-objective optimization.
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Abstract
Systems and methods are provided to engage in multi-objective optimization where there may be one or more constraints. At least one of the constraints may be soft constraints, such that if a potential solution to the multi-objective optimization problem violates only soft constraint(s), then that potential solution may be allowed to persist in a population of potential solutions that may be used to propagate child potential solutions. Potential solutions that violate soft constraints may be tested for non-domination sorting against other potential solutions that violate soft constraints and based at least in part on values associated with the soft constraint violations.
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20 Claims
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1. A method, comprising:
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identifying, by one or more processors, a first chromosome, wherein the first chromosome includes a plurality of decision variables to be optimized in a multi-objective optimization; identifying, by the one or more processors, a first constraint model corresponding to a first constraint and a second constraint model corresponding to a second constraint; identifying, by the one or more processors, the first constraint as a hard-constraint and the second constraint as a soft constraint; applying, by the one or more processors, the first constraint model to the first chromosome to derive a first constraint value; applying, by the one or more processors, the second constraint model to the first chromosome to derive a second constraint value; comparing, by the one or more processors, the first constraint value to a corresponding first threshold value to determine that the first chromosome does not violate the first constraint; comparing, by the one or more processors, the second constraint value to a corresponding second threshold value to determine that the first chromosome does violate the second constraint; indicating, by the one or more processors, that the first chromosome is soft infeasible, wherein a tag of soft infeasible indicates that only soft constraints have been violated; determining, based at least in part on the second constraint value, that the first chromosome is to be crossed-over with a second chromosome; generating a third chromosome by crossing over the first chromosome with the second chromosome; determining, based at least in part on the first constraint model and the second constraint model, that the third chromosome is feasible; and providing the third chromosome as an optimized 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; identify a first chromosome, wherein the first chromosome includes a plurality of decision variables to be optimized in a multi-objective optimization; identify a first constraint model corresponding to a first constraint and a second constraint model corresponding to a second constraint; identify the first constraint as a hard-constraint and the second constraint as a soft constraint; apply the first constraint model to the first chromosome to derive a first constraint value; apply the second constraint model to the first chromosome to derive a second constraint value; compare the first constraint value to a corresponding first threshold value to determine that the first chromosome does not violate the first constraint; compare the second constraint value to a corresponding second threshold value to determine that the first chromosome does violate the second constraint; indicate that the first chromosome is soft infeasible, wherein a tag of soft infeasible indicates that only soft constraints have been violated; determine, based at least in part on the second constraint value, that the first chromosome is to be crossed-over with a second chromosome; generate a third chromosome by crossing over the first chromosome with the second chromosome; determine, based at least in part on the first constraint model and the second constraint model, that the third chromosome is feasible; and provide the third chromosome as an optimized solution to the multi-objective optimization. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification