Optimization of Low Impact Development Layouts for Urban Stormwater Management: A Simulation-Based Approach Using Multi-Objective Scatter Search Algorithm

In recent years, the urgent need to mitigate stormwater runoff and address urban waterlogging has garnered significant attention. Low Impact Development (LID) has emerged as a promising strategy for managing urban runoff sustainably. However, the vast array of potential LID layout combinations presents challenges in quantifying their effectiveness and often results in high construction costs. To address these issues, this study proposes a simulation-optimization framework that integrates the Storm Water Management Model (SWMM) with advanced optimization techniques to minimize both runoff volume and costs. The framework incorporates random variations in rainfall intensity within the basin, ensuring robustness under diverse climatic conditions. By leveraging a multi-objective scatter search algorithm, this research optimizes LID layouts to achieve effective stormwater management. The algorithm is further enhanced by two local search techniques—namely, the ‘cost–benefit’ local search and path-relinking local search—which significantly improve computational efficiency. Comparative analysis reveals that the proposed algorithm outperforms the widely used NSGA-II (Non-dominated Sorting Genetic Algorithm II), reducing computation time by an average of 8.89%, 16.98%, 1.72%, 3.85%, and 1.23% across various scenarios. The results demonstrate the method’s effectiveness in achieving optimal LID configurations under variable rainfall intensities, highlighting its practical applicability for urban flood management. This research contributes to advancing urban sponge city initiatives by providing a scalable, efficient, and scientifically grounded solution for sustainable urban water management. The proposed framework is expected to support decision-makers in designing cost-effective and resilient stormwater management systems, paving the way for more sustainable urban development.