Particle Swarm Optimization Based Intelligent Smart City Parking Guidance System

Smart cities have the power to raise economic development, enhance the quality of life for city dwellers, and solve some of the problems that plague large metropolitan areas. We provide a Particle Swarm Optimization (PSO) approach to find optimal hyper parameter values for local machine learning models in a fuzzy logic (FL) scenario. Our technique is assessed by comparing the results of two case studies. We present a Particle Swarm Optimization (PSO)-based strategy for fine-tuning the hyperparameters of local ML models in a FL scenario. We analyse the results of two case studies to see how well our proposed method works. In order to estimate traffic patterns, we first take into account the services provided by smart cities and use a simulated environment built from an experimental transportation dataset. We think about cloud services and employ a real-time telemetry dataset to foretell when a machine would break down due to a component failure.