Enhancing design efficiency of intelligent garden space allocation: an adaptive layout algorithm by multi-objective ant colony optimisation

In complex urban environments, the design of urban gardens increasingly faces the challenge of balancing spatial efficiency, ecological function, and diverse human behavioural patterns. To address multi-objective optimisation scenarios, this study uses publicly available datasets: OpenStreetMap and GeoLife trajectory data. These datasets help extract urban garden structures and real-world human activity trajectories. Together, they form the basis for constructing spatial scenes and modelling behavioural constraints. Building on the classical Ant Colony Optimisation (ACO) framework, a multi-objective Pareto-front guidance mechanism is introduced to adaptively steer the search process. From a methodological perspective, this study proposes a dynamic heuristic function to enhance the algorithm's ability to address complex layout goals, including path connectivity, functional zoning diversity, and visual aesthetic harmony. This approach enables the algorithm to respond simultaneously to static spatial structures and dynamic behavioural patterns, achieving a synergistic optimisation between spatial layout and user needs.