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CAN PARETO FRONTS MEET THE SPLITTING CONDITION? COMPARING TWO GENERATIVE DESIGN ALGORITHMS BASED ON THE VARIETY OF DESIGN PARAMETERS COMBINATIONS THEY GENERATE

Published online by Cambridge University Press:  19 June 2023

Maxime Thomas ,
Lorenzo Nicoletti ,
Pascal Le Masson  and
Benoit Weil
Maxime Thomas*
Affiliation:
Mines de Paris; EPF-Ecole d'ingénieurs;
Lorenzo Nicoletti
Affiliation:
Technical University of Munich
Pascal Le Masson
Affiliation:
Mines de Paris;
Benoit Weil
Affiliation:
Mines de Paris;
*
Thomas, Maxime, Mines de Paris, France, [email protected]

Abstract

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Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.

Keywords

Generative Design AlgorithmsPareto FrontComputer Aided Design (CAD)Design processDesign theory
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 827 - 836
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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