Data-Efficient Design Exploration through Surrogate-Assisted Illumination

TL;DR

SAIL is a surrogate-assisted illumination algorithm that efficiently explores the design space, producing diverse high-quality solutions with significantly fewer evaluations than traditional methods, aiding designers in understanding possibilities beyond optimality.

Contribution

This paper introduces SAIL, a novel surrogate-assisted illumination algorithm that reduces the number of evaluations needed for diverse design exploration in complex optimization tasks.

Findings

SAIL produces hundreds of diverse, high-performing designs with fewer evaluations.

SAIL effectively maps high-performing designs in 3D aerodynamic problems.

SAIL outperforms MAP-Elites and CMA-ES in evaluation efficiency.

Abstract

Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a 2-dimensional airfoil optimization problem SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic 3-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.