Architectural space planning using evolutionary computing approaches: a review

Abstract

This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints.

Performative computational architecture using swarm and evolutionary optimisation: A review

“…may constitute relevant examples. Concerning the layout configuration, Dutta and Sarthak [14] compared applications of EC for architectural space planning. For sustainable building design, Evins [15] reviewed the application of computational optimisation by considering different research branches.…”

Gaps and requirements for automatic generation of space layouts with optimised energy performance

“…There are several review papers on GSL. Helme and Derix [36] collected the projects using GSL in practice; Dutta and Sarthak [37] solely focused on the application of evolutionary computing approaches for space layout design and did not categorise the GSL methods; Nassar [38] discussed the advances in graph theory and analysed their possibilities to be applied to architectural space layout design. The following review studies focus on the methods used for GSL: Frew in 1980 [39] categorised the methods based on whether the boundary was varied or not and how to change space dimensions; Hsu and Krawczyk in 2003 [40] introduced the methods used for space-planning programs separately regarding adjacency, representation, and different actions used among the design process; Lobos and Donath in 2010 [41] collected some relevant studies, but did not categorise the GSL methods; Calixto and Celani in 2015 [42] focused on the used evolutionary algorithms used for GSL.…”

Optimization of a User-Involved Interior Layout Recommendation System at the Operation Stage

“…Several academic and applied pieces of research were developed to investigate the generation and optimization of space planning with the use of computational architectural tools (Dutta and Sarthak, 2011). Boon et al (2015) created an analytical system intended to enhance efficiency within the groups of the complexlyinterconnected architectural program considering the adjacency between the spaces (Boon et al, 2015).…”