Energy-aware frugal Bayesian optimization

Abstract

Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.

Published in MATEC Web of Conferences 422, 02009. 7th International Conference on Engineering Optimization

References in corpus (4)

• pymoo: Multi-objective Optimization in Python
• The Hypervolume Indicator: Problems and Algorithms
• Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
• Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design