Brute-Force Optimization Workflow with Parallel Computing for Building Lifecycle Analysis: a Comparison with Multi-Objective Optimization with the Evolutionary Approach

To address the limitations of traditional optimization workflows in lifecycle analysis (LCA) during early design, this study proposes a Brute-Force Optimization (BFO) workflow with parallel simulation (PS) and data management. Using Python scripting and Grasshopper, the workflow automates design generation and PS and integrates carbon data. A comparative analysis with Multi-Objective Optimization (MOO) using Evolutionary Algorithms (EA) shows that within the same time frame, the BFO workflow optimized 2,187 design combinations, compared to 871 by MOO. The proposed workflow reduced total carbon emissions by $12 \%$ Energy Use Intensity (EUI) by $17 \%$, and cost by $17 \%$, offering greater flexibility in design tradeoffs. These results demonstrate the workflow’s potential for more comprehensive insights into sustainable building design optimization.