Humanized design strategy of urban public space based on multi-objective optimization algorithm

Current humanistic design of urban public spaces focuses on specific design elements while ignoring the conflicts and couplings between multiple user needs. This leads to spatial strategies stuck in local optima and lacking overall balance and adaptability. This paper constructs a multi-objective optimization model that integrates user preferences, multidimensional spatial indicators, and behavioral simulation. This model collects field data such as heat maps, path trajectories, and dwell time, identifies user types through K-means clustering, and models their spatial preferences using fuzzy membership functions. Design variables are set in Grasshopper; an optimization function is constructed; the optimal solution is searched using NSGA-III. Finally, pedestrian simulation is performed in AnyLogic, and the optimization results are corrected for function deviation to improve the coordination and adaptability of the design. Experimental results show that this strategy framework significantly improves spatial coordination, increasing weighted average satisfaction from 0.61 to 0.81 (+32.8%), reducing safety risks by 30.8% to 63.2%, and increasing interaction promotion by 71.2%. Multi-dimensional indicators verify the effectiveness of the optimization strategy in balancing user needs, alleviating local conflicts, and enhancing spatial adaptability, providing a quantitative basis and practical path for systematically solving the local optimal problem of humanized design of public spaces.