Multi-Objective Optimization in Space Planning: A Graph-Based Approach

Abstract This thesis investigates the integration of multi-objective optimization, space syntax, and graph analysis in the context of space planning for single-unit buildings. Focused on demonstrating the effectiveness of advanced techniques in generating diverse design options, the study emphasizes optimization based on specific criteria. Building regulations, client needs, and architectural considerations are central to achieving optimal solutions. The research methodology involves a comprehensive exploration of design possibilities that adeptly balances conflicting elements. Utilizing space syntax and graph analysis, the study visualizes relationships between spaces and movements within the building. The integration of sophisticated plugins, Termite Nest and Wallacei, within the Grasshopper platform enhances the analysis. Termite Nest employs space syntax and graph analysis to generate design options, while Wallacei leverages multi-objective optimization, specifically through genetic algorithms, to harmonize conflicting design considerations. This study contributes insights into the practical application of these advanced techniques in space planning, demonstrating their capacity to yield optimized design solutions aligned with specific criteria.