Abstract: In vehicle navigation systems,the aim of path planning techniques is to obtain the appropriate route for drivers.This problem and associated methods are very important in intelligent transportation  system.Finding the best route in complex urban streets is the typical nonlinear optimization problem.In these years,swarm intelligence algorithms are widely utilized in such issues and have shown some advantages.In this paper,some blocks in Tangshan city are modeled as nodes with related longitudes and latitudes. An improved fruit fly algorithm is designed for solving path planning task.Fitness function is adjusted for discrete optimization and some operators in genetic algorithm are introduced.Simulation experiments show that the algorithm designed in this paper is able to obtain a better route from the starting node to the destination.The cost of driving is reduced.

Key words: Vehicle navigation, path planning, fruit fly optimization, genetic algorithm, discrete optimization

摘要：

车载导航路径规划技术辅助驾驶员规划从起点到终点的合理路线，是智能交通系统的重要组成部分。在复杂城市街道中进行路径寻优是典型的非线性优化问题，近年来，群智能算法被广泛应用于该类复杂非线性问题的优化与求解。文中对唐山市第四幼儿园—市人大—市教育局街区进行建模，设计了一种用于车载导航路径规划的改进果蝇优化算法，对该区域的行车路线进行规划，算法采用了适用于路径规划问题的味道浓度函数，并引入遗传算法的部分算子。仿真实验表明，该算法能够快速高效地为车辆构建从起点到终点的行车路线，从而降低行车成本和减少能源损耗。

关键词: 车载导航, 路径规划, 果蝇算法, 遗传算法, 离散优化