Optimization Analysis of Multi-objective Renovation in Old Urban Areas under the Development of Smart Cities, Combining Subjective and Objective Weighting with PSO Algorithm

Current multi-objective algorithms face challenges in optimizing old city renovations within the context of smart city development due to the risk of getting trapped in local optima. To address this issue, a study developed an indicator system that integrates the Analytic Hierarchy Process with standards’ importance determination through assessing their correlation for weight evaluation. The particle swarm optimization algorithm was introduced to optimize the transformation plan, adjusting the inertia weight of particles, optimizing the particle swarm algorithm, and conducting simulation verification. Findings indicated a rapid surge in the number of overloaded nodes and pipe sections when the rainfall’s return period exceeded 5 years, illustrating the limited capacity of built sponge facilities to handle rainstorms. Under 50-year conditions, node and pipeline overload rates peaked at 31.70% and 50.19%, respectively. In combined facility simulations, runoff control rates increased to 82.55%-85.55% and 31.86%-36.97% under varying rainfall intensities. The proposed multi-objective optimization approach successfully defined optimal deployment ratios for four facilities, enhancing response capabilities under extreme rainfall conditions. This method demonstrates efficacy for transforming and optimizing old urban areas, especially in sponge city redevelopment efforts.