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首页> 外文会议>Proceedings of the 4th International Conference on Intelligent Logistics Systems >Route Planning for Car Navigation Systems by Combining Dijkstra Algorithm and Multi-objective Genetic Algorithms
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Route Planning for Car Navigation Systems by Combining Dijkstra Algorithm and Multi-objective Genetic Algorithms

机译：结合Dijkstra算法和多目标遗传算法的汽车导航系统路径规划

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摘要

Car navigation systems are to display the position of a car during running in real time,plan an optimalroute from the origin to the destination,and guide the ear to the destination along the optimal route. The routeselection is to search the optimal route whose length is shortest between two nodes in the road network model.The key problem of the selection is the shortest path problem. Dijkstra algorithm is a classical algorithm tosolve the shortest path problem. But the shortest path may not be the best one from the drivers' point of view in the real world. So,forpractical car navigation systems in real environment,we need to specify multiple and separate good choicesaccording to multiple criteria which make the search space be too large to find out the solution in real time bydeterministic algorithms. Genetic algorithms (GAs) are now widely used to solve routing search andoptimization problems. To be compared with Dijkstra algorithm,GAs can deal with several objectives at the same time. But GAswill spend longer time than Dijkstra algorithm to solve the same scale problem. To make up the disadvantagesof Dijkstra algorithm and GAs,we propose a method to combine the two algorithms in the paper.
机译：汽车导航系统将实时显示汽车的位置，规划从原点到目的地的最佳路线，并沿着最佳路线引导耳朵到目的地。路径选择是在道路网络模型中搜索两个节点之间长度最短的最优路径。选择的关键问题是最短路径问题。 Dijkstra算法是解决最短路径问题的经典算法。但是从现实世界中的驾驶员的角度来看，最短的路径可能并不是最佳的路径。因此，对于实际环境中的实用汽车导航系统，我们需要根据多个标准来指定多个单独的良好选择，这会使搜索空间太大，无法通过确定性算法实时找到解决方案。遗传算法（GA）现在被广泛用于解决路由搜索和优化问题。与Dijkstra算法相比，遗传算法可以同时处理多个目标。但是GA会比Dijkstra算法花费更多的时间来解决相同规模的问题。为了弥补Dijkstra算法和遗传算法的缺点，提出了一种将两种算法结合的方法。

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