Research on Path Optimization Based on Improved Adaptive Genetic Algorithm

Abstract

Path optimization, which can improve the travel efficiency of vehicle, has significances to time and cost saving. Path optimization mentioned in this article aims for optimizing total length and converts it into classical TSP to solve optimization problems and establish path optimization model. Based on this model, the improved adaptive genetic algorithm is put forward. This algorithm improves the population fitness sorting, adaptive crossover probability and mutation probability, etc. The comparison of simulation experiments shows that the improved adaptive genetic algorithm (AGA) has better global optimization ability and faster convergence speed than Simple Genetic Algorithm (SGA), which is the effective method to improve path optimization.