Sequential Optimization & Probabilistic Analysis Using Adaptively Refined Constraints in LS-OPT®

This paper presents some of the sequential optimization and probabilistic analysis methods in LS-OPT with particular emphasis on the use of classifiers for accuracy and efficiency improvement. Classifiers were first introduced in LS-OPT 6.0 for the handling of constraints. This paper provides a review of the basic classification-based constraint handling method and its applications and advantages for specific types of problems. Additionally, the application of classifiers is extended to adaptive sampling using EDSD (explicit design space decomposition) sampling constraints in LS-OPT 6.1. The different adaptive sampling options and approaches are presented through the examples. Another aspect of this paper is the extension of the probabilistic analysis method in LS-OPT from single iteration to sequential. The sequential analysis can be performed with or without EDSD sampling constraints, but sampling constraints, if used, are can guide the samples adaptively to important regions. Although the EDSD sampling constraints are defined using support vector machine (SVM) classifiers, the adaptive samples are useful in enhancing the constraint boundary accuracy even if it is defined using metamodels.