A fast genetic algorithm for solving architectural design optimization problems

Abstract

Building performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

Performative computational architecture using swarm and evolutionary optimisation: A review

“…in Ref. [90]. Furthermore, the trade-offs between functionality and cost were neglected both in the multi-objective and in weighted sum approaches…”

“…• Based on the sustainability objective in building layout problems [81,90], authors tended to use window dimensions (x15) instead of the window-to-wall ratio (x17). One reason is the fact that the window dimension parameters are more controllable in relation to variations in the layout…”

“…Chang and Shih [80] discussed initial and global costs. Su and Yan [90] stated and evaluated logistics. Finally, Yang, Ren, Turrin, Sariyildiz, and Sun [84], considered and analysed the structure.…”

“…Focussing on health campuses, Güleç Özer and Şener [130] used GA to find an optimal route of users in functionally complex buildings. Related to the healthcare facility, Su and Yan [90] optimised a nursing unit layout by maximising daylight illuminance and by minimising travel distances of nurses using GA. Dino [131] used EA to optimise unique fitness functions based on size, absolute dimension, compactness, jaggedness, convexity of space, as well as the façade, floor, neighbourhood, and separation criteria. The author focussed on a three-dimensional library building layout problem to implement the developed method.…”

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

“…Inspired from the nature, new optimization techniques are devised and being investigated in various fields recently (Eby et al ., 1999). Among these techniques, some have proved to be drastically effective in engineering-related subjects such as GA (Su and Yan, 2015), artificial ant colony (Rossi and Lanzetta, 2013), SA (Brown and Cagan, 1997), and PSO (Badamchizadeh et al ., 2010).…”