Learning the characteristics of engineering optimization problems with applications in automotive crash

Genetic and Evolutionary Computation Conference (GECCO), pp. 1227–1236

Abstract

Oftentimes the characteristics of real-world engineering optimization problems are not well understood. In this paper, we introduce an approach for characterizing highly nonlinear and Finite Element (FE) simulation-based engineering optimization problems, focusing on ten representative problem instances from automotive crashworthiness optimization. By computing characteristic Exploratory Landscape Analysis (ELA) features, we show that these ten crashworthiness problem instances exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used Black-Box Optimization Benchmarking (BBOB) problem set. Using clustering approaches, we demonstrate that these ten problem instances are clearly distinct from the BBOB test functions. Further analysis of the crashworthiness problem instances reveal that, as far as ELA concerns, they are most similar to a class of artificially generated functions. We identify such artificially generated functions and propose to use them as scalable and fast-to-evaluate representatives of the real-world problems. Such artificially generated functions could be used for the automated design of an optimization algorithm for specific real-world problem classes.

Authors 5

1. Fu Xing Long

   BMW (Germany) · BMW Group (Germany)

   Affiliation as printed

   BMW Group, Munich, Germany

2. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

3. Moritz A. Frenzel

   BMW (Germany) · BMW Group (Germany)

   Affiliation as printed

   BMW Group, Munich, Germany

4. Markus Gitterle

   Munich University of Applied Sciences

   Affiliation as printed

   University of Applied Sciences, Munich, Germany

5. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands