A study on multi-objective design exploration based on deep clustering and logistic regression

Abstract

Multi-objective design exploration is a framework to aim to extract knowledge to make rational decisions in multi-objective design problems. Since a set of Pareto solutions obtained by multi-objective topology optimization contains various types implicitly, it is difficult to extract the useful design knowledge by conventional data mining methods based on the objective function values. This study focuses on a framework for multi-objective design exploration that reveals the structure of the solution space by deep clustering and logistic regression for multi-objective topology optimization. In the framework, the deep clustering technique classifies the Pareto solution set in design variables space based on structural similarity to reveal representative types, then the logistic regression technique identifies the classification in evaluation criteria space that explains the differences in the types, and then designers arrange these results into design knowledge. This paper discusses its basic validity and possibilities through an application to a simple design problem of the conceptual design of a bridge structure.