Optimizing Sustainability and Performance of Green Roofs in Qeytarieh, Tehran: A Multi-Objective Approach

Abstract

This article presents an integrated multi-objective optimization model for designing sustainable green roofs in dense urban neighborhoods. The model incorporates four key objectives: minimizing energy consumption, costs, indoor thermal discomfort, and stormwater runoff. It employs a hybrid approach that combines the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), enhanced by human expert input to ensure the practical feasibility of the optimized solutions. The model's effectiveness was evaluated through a detailed case study in the densely populated Qeytarieh neighborhood of Tehran, Iran. The results demonstrated significant improvements compared to baseline conditions, including reductions of 15 percent in energy consumption, 13 percent in costs, 11 percent in thermal discomfort, and 29 percent in stormwater runoff volume. By providing urban designers with data-driven insights, this model enables informed decision-making regarding green infrastructure that aligns with project priorities and promotes sustainability and urban resilience. The key contributions of this research include: (1) the development of a hybrid MOEA/D-NSGA-II algorithm for multi-objective green roof optimization; (2) the careful consideration of multiple sustainability objectives simultaneously; and (3) the incorporation of human expertise through a creative logic-based optimization framework. The findings contribute to advancing sustainable urban development practices and provide practical guidelines for implementing optimized green roof systems in similar high-density urban contexts facing environmental challenges. Additionally, the study compares the performance of the proposed hybrid algorithm with existing optimization methods to demonstrate its superiority in achieving balanced sustainability outcomes.