Search - shortest path in map

Search - shortest path in map - List

[AI-NN-PR] Genetic最短路径 DL : 0: 在vc控制台中运行，用遗传算法求解最短路径，用邻接矩阵表示路线图。-in vc console operations, Genetic Algorithm for the shortest path, adjacency matrices road map.
Date : 2026-01-02 Size : 123kb User : rabo
[AI-NN-PR] rengongzhineng DL : 0: 利用图搜索技术求解状态图中任意两点的路径和最短路径，并利用这种方法解决旅行商问题-Use map search technology to solve any two points in the state diagram of the path and the shortest path, and take advantage of this approach to solve traveling salesman problem
Date : 2026-01-02 Size : 29kb User : 川原
[AI-NN-PR] FindRouteGUI DL : 0: 1，已完成功能（1）距离短时大部分情况下可以选择最短路径进行移动（2）加入了精英选择优化算法（3）无论地图大小一定可以得出路径（4）点击障碍物可以得出路径 3,代码改进将原来两大类（Widget，Geno）拆分成三个类（Widget，Handle，Geno）， Widget：显示界面等可视化元素，获取最短路径并显示移动， Handle：优化路径，得出最短路径，以后会加入通信模块 Geno：计算得出一定数量的可达路径 4，算法描述（1）遗传算法：根据起始点的曼哈顿距离得出染色体的大约长度，最好比曼哈顿距离稍大。在计算过程中如果没法得出可达路径（则适应性分数不等于1）会自动加大染色体长度。 -1, has completed the function (1) short distance in most cases you can choose the shortest path to move (2) joined the elite selection optimization algorithm (3) regardless of the size of the map can be drawn a certain path (4) Click on the path obstructions can be drawn 3, code improvements The original two categories (Widget, Geno) split into three categories (Widget, Handle, Geno), Widget: display interface and other visual elements, to obtain the shortest path and display the movement Handle: optimal path, draw the shortest path, the communication module will be added later Geno: up to a certain number of calculated path 4, the algorithm described (1) genetic algorithm: According to arrive at the starting point of the Manhattan distance about the length of the chromosome, preferably slightly larger than the Manhattan distance. If in the calculation process can not come up the path (if not equal to 1 adaptability scores) will automatically increase the
Date : 2026-01-02 Size : 10kb User : chan
[AI-NN-PR] zhangkui DL : 0: 考虑智能水下机器人在水下行走，受二维海流的影响程度-An improved self-organizing map-based approach is proposed for multiple autonomous underwater vehicle (multi-AUV) system to complete the allocation of targets and path planning in the paper. Each target is to be visited by one and only one AUV, and a shortest path between a starting point and the destination for each AUV in the presence of the variable current environment is to be found. Firstly, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 2-D ocean environment. Then, the velocity synthesis approach is applied to plan a shortest path for each AUV to visit the corresponding target in dynamic environment subject to the ocean current being variable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.
Date : 2026-01-02 Size : 221kb User : 张逵