Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm

Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only introduces particle filters, which are advanced visual tracking, but also improves the cuckoo search algorithm; when the bird’s egg is found by the bird’s nest owner, it needs to randomly change the position of the entire bird’s nest, which speeds up the search speed of the optimal delivery route. Analyze and compare the hybrid intelligent algorithm and the cuckoo search algorithm. Finally, the international standard test set Benchmark Problems is used for testing. The experimental outcomes indicated that the new hybrid intelligent approach is an effective algorithm for handling vehicle routing tasks with time windows.