| id |
acadia15_357 |
| authors |
Ashour, Yassin; Kolarevic, Branko |
| year |
2015 |
| title |
Heuristic Optimization in Design |
| doi |
https://doi.org/10.52842/conf.acadia.2015.357
|
| source |
ACADIA 2105: Computational Ecologies: Design in the Anthropocene [Proceedings of the 35th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-53726-8] Cincinnati 19-25 October, 2015), pp. 357-369 |
| summary |
This paper presents a workflow called the ‘heuristic optimization workflow’ that integrates Octopus, a Multi-Objective Optimization (MOO) engine with Grasshopper3D, a parametric modeling tool, and multiple simulation software. It describes a process that enables the designer to integrate disparate domains via Octopus and complete a feedback loop with the developed interactive, real-time visualization tools. A retrospective design of the Bow Tower in Calgary is used as a test case to study the impact of the developed workflow and tools, as well as the impact of MOO on the performance of the solutions. The overall workflow makes MOO based results more accessible to designers and encourages a more interactive ‘heuristic’ exploration of various geometric and topological trajectories. The workflow also reduces design decision uncertainty and design cycle latency through the incorporation of a feedback loop between geometric models and their associated quantitative data. It is through the juxtaposition of extreme performing solutions that serendipity is created and the potential for better multiple performing solutions is increased.es responsive systems, which focus on the implementation of multi-objective adaptive design prototypes from sensored environments. The intention of the work is to investigate multi-objective criteria both as a material system and as a processing system by creating prototypes with structural integrity, where the thermal energy flow through the prototype, to be understood as a membrane, can be controlled and the visual transparency altered. The work shows performance based feedback systems and physical prototype models driven by information streaming, screening, and application. |
| keywords |
Multi-Objective Optimization, Generative Design, Performance-Based Design |
| series |
ACADIA |
| type |
normal paper |
| email |
|
| full text |
 file.pdf (8,911,893 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:54 |
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