Application of Improved Ant Colony Optimization Algorithm in Urban Ecological Landscape Spatial Layout Optimization Method

Traditional spatial planning methods have many limitations when facing complex urban environments. Therefore, this study introduces an urban ecological landscape spatial layout optimization method based on improved ACO (Ant Colony Optimization). By introducing new optimization strategies and parameter adjustments, the global search capability and convergence speed of the algorithm are improved. This study first defines different functional areas in the urban ecological landscape as nodes, and the connecting roads or ecological corridors between nodes as paths, and constructs a weighted graph model. Then, the algorithm is initialized by setting key parameters such as the number of ants, pheromone factor, heuristic function factor, etc. Next, various improvement strategies such as adaptive pheromone adjustment, pheromone range limitation, dynamic local pheromone update, continuous space adaptation and simulated annealing embedding are applied. In the path selection and search phase, ants select the next node based on pheromone concentration and heuristic information to form a complete path. Finally, pheromones are updated according to the path quality and a new round of iteration is started until the termination condition is met. The improved algorithm also shows better optimization effects in terms of ecological connectivity index, floor area ratio and green space coverage. In 20 layout optimization tasks, the improved algorithm improves the ecological connectivity index, the floor area ratio increases from an average of 1.54 to 2.8, and the maximum green coverage rate increases from 27.6% to 42.8%. These results not only verify the effectiveness of the improved algorithm but also demonstrate its wide applicability and strong optimization capabilities in different urban ecological landscape layouts.