Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization

Abstract

Parametric tools in architecture allow for the design of complex and multi-dimensional forms on building facades. Among these, geometric patterns can mitigate direct sunlight and enhance energy efficiency. However, conventional simulation methods and available optimization tools are prohibitively expensive for optimizing such complex forms. To address this challenge, this study proposes two innovative hybrid workflows that integrate parametric modeling, evolutionary approximate and accurate models (NSGA-III), clustering through k-means algorithm, and local search (Tabu search) technique. The outcomes obtained from employing these hybrid approaches demonstrate a substantial reduction in computational time and costs while simultaneously achieving optimal results. Additionally, an extensive comparison between the two proposed methodologies is presented encompassing factors such as performance metrics and computational expenses incurred during implementation. The findings derived from pattern optimization reveal several key insights: increasing pattern counts; dispersing them across the facade; minimizing the distance between the pattern wall from windows; adopting a south-facing orientation with positive vertical rotation – all contribute towards diminishing energy consumption (measured by Energy Use Intensity or EUI) within cold climates. However, material selection for these patterns primarily affects visual comfort levels.