Optimizing Environmental Performance in High-Density Residential Neighborhoods: A Generative Approach Using Urban Form attributes and Genetic Algorithms

Abstract

This study addresses the critical need to optimize environmental performance in high-density residential neighborhoods, focusing on Ahmedabad, India—facing rapid redevelopment scenario in urban core. The research examines how urban form attributes, including density, geometry, land use, and typologies, influence the environmental performance and energy efficiency of neighborhoods. Employing a generative design approach, this study applies a multi-objective genetic algorithm (GA) to balance environmental performance criteria such as sunlight hours, solar radiation, sky view factor, and surface to volume ratio (SA/V). Using computational tools, including Rhino/Grasshopper and the Biomorpher plugin, the methodology iteratively generates and refines configurations of street networks, open spaces, and building layouts that meet specific site and climate conditions. The optimization process, involving an initial population of 48 design solutions and guided by fitness criteria, evolves through three generations to identify configurations that align with performance goals.These findings underscore the adaptability of the generative approach, enabling urban designers to incorporate site specific environmental objectives into preliminary design phases. This study provides a replicable framework for creating resilient, energy-efficient, and environmentally sustainable neighborhoods, offering a valuable tool for urban planners addressing the challenges of dense urban environments.