Systems and methods are provided for performing multi-objective optimizations with a relatively large number of objectives to which optimization is to be performed. The objectives of the optimization problem may be partitioned to two or more subsets (e.g., overlapping or non-overlapping subsets) of objectives, and partial optimization(s) may be performed using a subset or combination of subsets of the objectives. One or more of the partial optimizations may use one or more pareto-optimized chromosomes from a prior partial optimization. A final full optimization may be performed according to all of the objectives of the optimization problem and may use one or more chromosomes of any preceding partial optimization as a starting point for finding a final solution to the optimization problem. Any variety of processes may be employed to mitigate archive explosion that may be associated with relatively large objective sets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: identifying, by one or more processors of a multi-objective evolutionary algorithm system, a plurality of objectives associated with a multi-objective optimization problem; performing, by the one or more processors, a partial heuristic optimization according to a first set of objectives to generate a first optimized chromosome, wherein the first set of objectives comprise one or more of the plurality of objectives, and wherein the first optimized chromosome is a pareto-optimized solution of the partial heuristic optimization; performing, by the one or more processors, a full heuristic optimization according to the plurality of objectives, wherein the first optimized chromosome is used as a starting point for the full heuristic optimization; and providing, by the one or more processors and based at least in part on the full heuristic optimization, a solution to the multi-objective optimization problem. 2. The method of claim 1, wherein the partial heuristic optimization is a first partial heuristic optimization and further comprising: performing, by the one or more processors, a second partial heuristic optimization according to a second set of objectives to generate a second optimized chromosome, wherein the second set of objectives comprise one or more of the plurality of objectives, and wherein the second optimized chromosome is a pareto-optimized solution of the second partial heuristic optimization; and wherein performing the first partial heuristic optimization comprises using the second optimized chromosome as a starting point for the first partial heuristic optimization. 3. The method of claim 2, wherein the first set of objectives includes a first objective and a second objective, and wherein the second set of objectives includes at least one of: (i) the first objective; or (ii) different ones of the plurality of objectives than the first set of objectives. 4. The method of claim 1, wherein performing the full heuristic optimization further comprises appending one or more genes to the first optimized chromosome, wherein the one or more genes are associated with the full set of objectives. 5. The method of claim 1, wherein performing the partial heuristic optimization comprises: determining a number of objectives for which the first optimized chromosome is non-dominated by a plurality of chromosomes associated with the first partial optimization; and archiving the first optimized chromosome identifying the number of objectives for which the first optimized chromosome is non-dominated. 6. The method of claim 1, wherein performing the partial heuristic optimization comprises: identifying a subset of the first set of objectives; determining a number of objectives from the subset of the first set of objectives for which the first optimized chromosome is non-dominated by a plurality of chromosomes associated with the first partial optimization; and archiving the first optimized chromosome identifying the number of objectives for which the first optimized chromosome is non-dominated. 7. The method of claim 1, wherein performing the partial heuristic optimization comprises: identifying a plurality of metrics, each of the metrics derived from values associated with the first set of objectives; determining values corresponding to each of the plurality of metrics for the first optimized chromosome and a plurality of chromosomes associated with the first partial optimization; determining a number of metrics from the plurality of metrics for which the first optimized chromosome is non-dominated by the plurality of chromosomes associated with the first partial optimization; and archiving the first optimized chromosome identifying the number of metrics for which the first optimized chromosome is non-dominated. 8. The method of claim 1, wherein performing the partial heuristic optimization comprises: identifying a second non-dominated chromosome; identifying a third non-dominated chromosome; and performing a cross-over operation with the second non-dominated chromosome and the third non-dominated chromosome to generate the first optimized chromosome, wherein the second non-dominated chromosome is selected from among a fourth non-dominated chromosome and the second non-dominated chromosome, wherein the second non-dominated chromosome is non-dominated based on a greater number of objectives or metrics than the fourth non-dominated chromosome. 9. The method of claim 1, wherein performing the partial heuristic optimization to generate the first optimized chromosome further comprises: selecting a first parent chromosome and selecting a second parent chromosome; identifying a first gene of the first optimized chromosome; identifying a first parent gene of the first parent chromosome, the first parent gene corresponding to the first gene; identifying a second parent gene of the second parent chromosome, the second parent gene corresponding to the first gene; identifying a hinting gene of the first parent chromosome; and determining a value of the first gene based at least in part on the first parent gene, the second parent gene, and the hinting gene. 10. The method of claim 1, further comprising selecting a resilient chromosome from a dominated epsilon space, and based at least in part on at least one of: (i) a Euclidean distance of the resilient chromosome from a utopia corner of a non-dominated epsilon space; or (ii) a determination that at least one the resilient chromosome dominates a non-dominated chromosome according to at least one objective. 11. 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 objectives associated with a multi-objective optimization problem; perform a partial heuristic optimization according to a first set of objectives to generate a first optimized chromosome, wherein the first set of objectives comprise one or more of the plurality of objectives, and wherein the first optimized chromosome is a pareto-optimized solution of the partial heuristic optimization; perform a full heuristic optimization according to the plurality of objectives, wherein the first optimized chromosome is used as a starting point for the full heuristic optimization; and provide, based at least in part on the full heuristic optimization, a solution to the multi-objective optimization problem. 12. The system of claim 11, wherein the partial heuristic optimization is a first partial heuristic optimization and wherein the at least one processor is further configured to execute the computer-executable instructions to: perform a second partial heuristic optimization according to a second set of objectives to generate a second optimized chromosome, wherein the second set of objectives comprise one or more of the plurality of objectives, and wherein the second optimized chromosome is a pareto-optimized solution of the second partial heuristic optimization, and wherein performing the first partial heuristic optimization comprises using the second optimized chromosome as a starting point for the first partial heuristic optimization. 13. The system of claim 12, wherein the first set of objectives includes a first objective and a second objective, and wherein the second set of objectives includes at least one of: (i) the first objective; or (ii) different ones of the plurality of objectives than the first set of objectives. 14. The system of claim 12, wherein the at least one processor configured to perform the full heuristic optimization comprises the at least one processor configured to append one or more genes to the first optimized chromosome, wherein the one or more genes are associated with the full set of objectives. 15. The system of claim 11, wherein the at least one processor configured to perform the partial heuristic optimization comprises the at least one processor configured to: determine a number of objectives for which the first optimized chromosome is non-dominated by a plurality of chromosomes associated with the first partial optimization; and archive the first optimized chromosome identifying the number of objectives for which the first optimized chromosome is non-dominated. 16. The system of claim 11, wherein the at least one processor configured to perform the partial heuristic optimization comprises the at least one processor configured to: identify a subset of the first set of objectives; determine a number of objectives from the subset of the first set of objectives for which the first optimized chromosome is non-dominated by a plurality of chromosomes associated with the first partial optimization; and archive the first optimized chromosome identifying the number of objectives for which the first optimized chromosome is non-dominated. 17. The system of claim 11, wherein the at least one processor configured to perform the partial heuristic optimization comprises the at least one processor configured to: identify a plurality of metrics, each of the metrics derived from values associated with the first set of objectives; determine values corresponding to each of the plurality of metrics for the first optimized chromosome and a plurality of chromosomes associated with the first partial optimization; determine a number of metrics from the plurality of metrics for which the first optimized chromosome is non-dominated by the plurality of chromosomes associated with the first partial optimization; and archive the first optimized chromosome identifying the number of metrics for which the first optimized chromosome is non-dominated. 18. The system of claim 11, wherein the at least one processor configured to perform the partial heuristic optimization comprises the at least one processor configured to: identify a second non-dominated chromosome; identify a third non-dominated chromosome; and perform a cross-over operation with the second non-dominated chromosome and the third non-dominated chromosome to generate the first optimized chromosome, wherein the second non-dominated chromosome is selected from among a fourth non-dominated chromosome and the second non-dominated chromosome, wherein the second non-dominated chromosome is non-dominated based on a greater number of objectives or metrics than the fourth non-dominated chromosome. 19. The system of claim 11, wherein the at least one processor configured to perform the partial heuristic optimization to generate the first optimized chromosome comprises the at least one processor configured to: select a first parent chromosome and selecting a second parent chromosome; identify a first gene of the first optimized chromosome; identify a first parent gene of the first parent chromosome, the first parent gene corresponding to the first gene; identify a second parent gene of the second parent chromosome, the second parent gene corresponding to the first gene; identify a hinting gene of the first parent chromosome; and determine a value of the first gene based at least in part on the first parent gene, the second parent gene, and the hinting gene. 20. The system of claim 11, wherein the at least one processor is further configured to execute the computer-executable instructions to: identify that the first optimized chromosome and a second optimized chromosome are in a same epsilon box; perform an analysis to determine that both the first optimized chromosome and the second optimized chromosome are to be stored in an archive checkpoint, wherein the analysis comprises at least one of: (i) a non-domination sorting between the first optimized chromosome and the second optimized chromosome, (ii) a determination of a Euclidean distance for each of the first optimized chromosome and the second optimized chromosome, (iii) a non-domination sorting between the first optimized chromosome and the second optimized chromosome using reduced sets of objective metrics, (iv) a determination of a best performing chromosome for each objective metric, or (v) a determination of a first genetic diversity of the first optimized chromosome and a second genetic diversity of the second chromosome; and store, based at least in part on the analysis, both the first chromosome and the second chromosome in an archive checkpoint.