Urban Public Space Design using Genetic Algorithm and Ant Colony Optimization

Now a days, the Urban public spaces plays an essential role in influencing sustainability, resilience as well as livability of cities. Effective design of urban spaces enhances quality of life, promotes sustainability, and fosters economic growth. However, the Urban public spaces lack with the community involvement, and insensitive design which leads to underutilized, unsafe, or uninviting public spaces. To overcome these drawbacks in this research a Genetic Algorithm (GA) with Ant Colony Optimization (ACO) is applied for designing public spaces for urban areas. Initially, virtual 3D Building Information Modelling (BIM) model is constructed on the basis of 2D digital layout design then, 3D images are incorporated with the BIM design software. Further, the virtual 2D drafting is used to change the hard copies of 2D design sketches. Then, the GA optimizes the combination of sensing reports from cooperative users and searches for the set of optimal coefficient vectors to reduce error probability. Finally, ACO is employed for optimization of space utilization and logistics. From the results, the proposed GA with ACO achieved better results with the working performance of 96.92% when compared to the existing Virtual Reality based Landscape Planning Effect Simulation (VR-LPES) respectively.