A Comparison of Galapagos and Wallacei Optimization Solvers in High-Rise Building Design

Authors : Ceren Aydan Nasır, Funda Tan Bayram, Seher Güzelçoban Mayuk

Pages : 131-153

Doi:10.60093/jiciviltech.1585588

View : 115 | Download : 357

Publication Date : 2025-12-31

Article Type : Research Paper

Abstract :Optimizing designs that meet specific criteria is burdensome for designers and slows down the building production process. Innovative tools can help by performing repetitive and complex calculations quickly to efficiently reach optimization goals. Optimization solvers facilitate this process by generating design variations suitable for single and multiple objectives. This study examines the advantages and capabilities of optimization solvers in high-rise building design. Following a literature review on ML-based tools, the study focused on the Galapagos and Wallacei solvers. A basic parametric high-rise model was created, defining a design problem at two levels of complexity. With each solver, the most suitable design variations for these problems were generated and compared in terms of interface, working mechanisms, effectiveness, and practical contributions. The analyses conducted revealed that Machine Learning (ML) contributes to parametric design processes. The comparison of Galapagos and Wallacei solvers provides a basic understanding of the subject through a simple example. Thus, it has created a different example in this context in terms of the practical applicability of these tools. Furthermore, within the scope of the study, recommendations were made to increase interface usability for different design contexts.
Keywords : Optimizasyon, Yüksek Yapılar, Optimizasyon Çözücüleri, Galapagos, Wallacei

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