基于改进的自适应遗传算法路径优化研究

摘要

路径优化可以提高车辆行驶效率，为人们节省时间和成本。路径优化以总长度为优化目标，将其转换为经典TSP优化问题进行求解并建立路径优化模型，在此模型上提出改进的自适应遗传算法。该算法通过改进可实现自适应交叉概率以及变异概率。通过与简单遗传算法(Rank)的对比仿真实验，结果表明，改进的自适应遗传算法有较好的全局寻优能力，且其收敛速度快，是解决路径优化问题的有效方法。%Path optimization, which can improve the travel efficiency of vehicles, has significances in time and cost saving. Path optimization mentioned in this article aims for optimizing the total length and converts it into classical TSP to solve optimization problems and establishes path optimization model. Based on this model, the improved adaptive genetic algorithm is put forward. This algorithm improves the populationfitness 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), hence an effective method to promote path optimization.