Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are provided to engage in multi-objective optimization where there may be potential solutions for evaluation (e.g., chromosomes) that each have one or more conditional genes. The value of each of the conditional genes in each of the chromosomes may be equivalent to one of a plurality of hidden genes in each of the chromosomes. The value of each of the conditional genes may be evaluated prior to determining objective values of each of the chromosomes. The objective values of each of the chromosomes may be used to evaluate the potential solutions embodied in the chromosomes and further drive to more optimized solutions. The use of the conditional genes in the chromosomes may reduce the amount of constraint violation checks that may need to be performed.

Abstract: 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.

Abstract: Systems and methods are provided to engage in multi-objective optimization where there may be potential solutions for evaluation (e.g., chromosomes) that each have one or more conditional genes. The value of each of the conditional genes in each of the chromosomes may be equivalent to one of a plurality of hidden genes in each of the chromosomes. The value of each of the conditional genes may be evaluated prior to determining objective values of each of the chromosomes. The objective values of each of the chromosomes may be used to evaluate the potential solutions embodied in the chromosomes and further drive to more optimized solutions. The use of the conditional genes in the chromosomes may reduce the amount of constraint violation checks that may need to be performed.

Abstract: Systems and methods are provided to determine launch parameters of satellites of a satellite constellation that provides optimized performance of the satellite constellation over the service lifetime of the satellite constellation. The launch parameters may be determined by considering perturbing accelerations of one or more of the satellites for the purposes of optimizing the launch parameters of the satellites of the satellite constellation. The systems and methods may include heuristic optimization and high-fidelity astrodynamic modeling methodologies.

Abstract: Systems and methods are provided to determine launch parameters of satellites of a satellite constellation that provides optimized performance of the satellite constellation over the service lifetime of the satellite constellation. The launch parameters may be determined by considering perturbing accelerations of one or more of the satellites for the purposes of optimizing the launch parameters of the satellites of the satellite constellation. The systems and methods may include heuristic optimization and high-fidelity astrodynamic modeling methodologies.

Abstract: Embodiments of the invention may provide systems and methods for supporting restricted search capabilities in high-dimensional spaces. These example restricted search capabilities may allow for an unbiased search that is simply restricted to those regions of interest to a decision maker. It will be appreciated that a restricted search does not mean that additional constraints, such as preference or biasing information, are utilized to reduce the search space into some feasible sub-space of the original optimization problem. Instead, the example restricted search may limit the search to a certain sub-space of the full multi-dimensional tradeoff space.

Abstract: Systems and methods may include an application program interface that enables a user to: specify parameters associated with an evolutionary algorithm, where an execution of the evolutionary algorithm is in accordance with the specified parameters; define a chromosome data structure that includes a plurality of variables that are permitted to evolve in value in accordance with the execution of the evolutionary algorithm in order to generate one or more child chromosome data structures; identify one or more objective functions for evaluating chromosome data structures, including the generated one or more child chromosome data structures; and define an output format for providing one or more optimal chromosome data structures of the evaluated generated child chromosome data structures as designs to the identified objective functions.

Abstract: Systems and methods are provided for a core management system for parallel processing of an evolutionary algorithm.

Abstract: The systems and methods may include receiving an initial population of parent chromosome data structures, where each parent chromosome data structure provides a plurality of genes; selecting pairs of parent chromosome data structures; applying at least one evolutionary operator to the genes of the selected pairs to generate a plurality of child chromosome data structures; allocating, the generated plurality of child chromosome structures to a plurality slave processors, where each slave processor evaluates one or more of the plurality of child chromosome data structures and generates respective objective function values; receiving objective function values for a portion of the plurality of allocated child chromosome data structures; merging the parent chromosome data structures with the received portion of the child chromosome data structures for which objective function values have been received; and identifying a portion of the merged set of chromosome data structures as an elite set of chromosome data stru

Abstract: Embodiments of the invention may provide systems and methods for supporting restricted search capabilities in high-dimensional spaces. These example restricted search capabilities may allow for an unbiased search that is simply restricted to those regions of interest to a decision maker. It will be appreciated that a restricted search does not mean that additional constraints, such as preference or biasing information, are utilized to reduce the search space into some feasible sub-space of the original optimization problem. Instead, the example restricted search may limit the search to a certain sub-space of the full multi-dimensional tradeoff space.

Abstract: Systems and methods may include an application program interface that enables a user to: specify parameters associated with an evolutionary algorithm, where an execution of the evolutionary algorithm is in accordance with the specified parameters; define a chromosome data structure that includes a plurality of variables that are permitted to evolve in value in accordance with the execution of the evolutionary algorithm in order to generate one or more child chromosome data structures; identify one or more objective functions for evaluating chromosome data structures, including the generated one or more child chromosome data structures; and define an output format for providing one or more optimal chromosome data structures of the evaluated generated child chromosome data structures as designs to the identified objective functions.

Abstract: The systems and methods may include receiving an initial population of parent chromosome data structures, where each parent chromosome data structure provides a plurality of genes; selecting pairs of parent chromosome data structures; applying at least one evolutionary operator to the genes of the selected pairs to generate a plurality of child chromosome data structures; allocating, the generated plurality of child chromosome structures to a plurality slave processors, where each slave processor evaluates one or more of the plurality of child chromosome data structures and generates respective objective function values; receiving objective function values for a portion of the plurality of allocated child chromosome data structures; merging the parent chromosome data structures with the received portion of the child chromosome data structures for which objective function values have been received; and identifying a portion of the merged set of chromosome data structures as an elite set of chromosome data stru

Abstract: Systems and methods are provided for a core management system for parallel processing of an evolutionary algorithm.