Publication | Design Modeling Symposium 2017

Nature-based Hybrid Computational Geometry System for Optimizing Component Structure


Nature-based Hybrid Computational Geometry System for Optimizing Component Structure

Danil Nagy, Dale Zhao, David Benjamin

Design Modeling Symposium 2017

This paper describes a novel computational geometry system developed for application in the design of full-scale industrial components. This system combines a bottom-up growth strategy based on slime mold behaviour in nature with a top-down genetic algorithm strategy for optimization. The growth strategy uses an agent-based algorithm to create individual instances of designs based on a small number of input parameters. These parameters can then be controlled by a genetic algorithm to optimize the final design according to goals such as minimizing weight and minimizing structural weakness. Together, these two strategies create a hybrid approach which ensures high performance while allowing the designer to explore a wider range of novel designs than would be possible using traditional design methods.

Download publication

Related Resources



Generative design for COVID-19 and future pathogens using stochastic multi-agent simulation

Proposing a generative design workflow that integrates a stochastic…



Recently Published by Autodesk Researchers

A selection of recently published papers by Autodesk Researchers…



Generative Urban Design: Integration of financial and energy design goals in a generative design workflow for residential neighborhood layout

This paper demonstrates an application of Generative Design to an…



Survey-Based Simulation of User Satisfaction for Generative Design in Architecture

This paper describes a novel humanist approach to generative design…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us