Nature-based Hybrid Computational Geometry System for Optimizing Component Structure

Abstract

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.