Performative computational architecture using swarm and evolutionary optimisation

A review

Journal article (2019)

Authors

B. Ekici Yasar University, Design Informatics - Architecture and the Built Environment
C. Çubukçuoglu Design Informatics - Architecture and the Built Environment , Yasar University
M. Turrin Design Informatics - Architecture and the Built Environment
I.S. Sariyildiz Design Informatics - Architecture and the Built Environment
Research Group
Design Informatics (Architecture and the Built Environment) (TU Delft)
Copyright: © 2019 B. Ekici, C. Çubukçuoglu, M. Turrin, I.S. Sariyildiz
DOI
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https://doi.org/10.1016/j.buildenv.2018.10.023
Evolutionary algorithm Performance-based design Architectural design Swarm intelligence Building design Computational optimisation
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Architecture and the Built Environment

Department

Architectural Engineering +Technology

Research Group

Design Informatics

Abstract

This study presents a systematic review and summary of performative computational architecture using swarm and evolutionary optimisation. The taxonomy for one hundred types of studies is presented herein that includes different sub-categories of performative computational architecture, such as sustainability, cost, functionality, and structure. Specifically, energy, daylight, solar radiation, environmental impact, thermal comfort, life-cycle cost, initial and global costs, energy use cost, space allocation, logistics, structural assessment, and holistic design approaches, are investigated by considering their corresponding performance aspects. The main findings, including optimisation and all the types of parameters, are presented by focussing on different aspects of buildings. In addition, usage of form-finding parameters of all reviewed studies and the distributions for each performance objectives are also presented. Moreover, usage of swarm and evolutionary optimisation algorithms in reviewed studies is summarised. Trends in publications, published years, problem scales, and building functions, are examined. Finally, future prospects are highlighted by focussing on different aspects of performative computational architecture in accordance to the evidence collected based on the review process.