Systems and methods for auto-adaptive control over converged results for multi-dimensional optimization

摘要

Systems and methods may include identifying an input population of parent epsilon chromosome data structures; combining genes of each selected pair of parent epsilon chromosome data structures according to at least one evolutionary operator to generate a plurality of child epsilon chromosome data structures, each child epsilon chromosome data structure providing one or more genes each having a respective candidate epsilon value representing a respective step size or spacing for the respective problem objective; and evaluating each of the plurality of child epsilon chromosome data structures according to one or more epsilon objective functions to generate respective epsilon objective function values for each child epsilon chromosome data structure, where each epsilon objective function is associated with a respective goal associated with at least one a priori criterion, where each respective epsilon objective function value indicates an extent to which each respective goal can be achieved.

主权项

1. A method comprising:
identifying an input population of parent epsilon chromosome data structures, wherein each parent epsilon chromosome data structure provides genes each having a respective candidate epsilon value, each candidate epsilon value representing a respective step size or spacing associated with a respective problem objective of a plurality of problem objectives; selecting one or more pairs of parent epsilon chromosome data structures from the input population of parent epsilon chromosome data structures; combining genes of each selected pair of parent epsilon chromosome data structures according to at least one evolutionary operator to generate a plurality of child epsilon chromosome data structures, each child epsilon chromosome data structure providing one or more genes each having a respective candidate epsilon value representing a respective step size or spacing for the respective problem objective; and evaluating each of the plurality of child epsilon chromosome data structures according to one or more epsilon objective functions to generate respective epsilon objective function values for each child epsilon chromosome data structure, wherein each epsilon objective function is associated with a respective goal associated with at least one a priori criterion defined using at least a respective subset of the plurality of problem objectives, wherein each respective epsilon objective function value indicates an extent to which each respective goal can be achieved, wherein the prior steps are performed by one or more computers wherein the identifying, selecting, combining, and evaluating steps form an epsilon optimization process, and further comprising: selecting an epsilon chromosome data structure from one of the evaluated chromosome data structures, wherein candidate epsilon values from the selected epsilon chromosome data structure form an epsilon vector utilized in performing a problem optimization process, wherein the problem optimization process seeks to identify a set of epsilon non-dominated solutions for each respective subset of the plurality of problem objectives, wherein a size of the set of solutions for each subset is based at least in part on the epsilon vector.