Towards equitable and immersive outdoor orienteering: an artificial intelligence-driven multi-objective route planning framework with augmented sand cat swarm optimization

README: Towards equitable and immersive outdoor orienteering: an artificial intelligence-driven multi-objective route planning framework with augmented sand cat swarm optimization Description of the data and file structure This dataset contains all MATLAB source code, simulated terrain data, and optimization results generated for the study "Towards equitable and immersive outdoor orienteering...". Data were generated through computational simulations in MATLAB R2024b. Fifty random terrain maps (6000m × 6000m each) were created by superimposing 2048 randomly placed convex areas to simulate realistic terrain undulations. The code implements five design principles for orienteering route design (ORD) and solves the multi-objective optimization problem using Sand Cat Swarm Optimization (SCSO), compared with Particle Swarm Optimization (PSO) and Dung Beetle Optimizer (DBO) algorithms. Route lengths between checkpoints were calculated using the Artificial Potential Field (APF) method to account for terrain obstacles. Files and variables File: Supporting_Information_files.zip Description: File Structure and Descriptions The dataset is organized into the following folders: /code: Contains all MATLAB source code. /terrain_data: Contains all simulated terrain data, organized into subfolders terrain_data1 to terrain_data4 (Maps 1-50). /code_results: Contains all raw output results from optimization runs (convergence curves, fitness values, optimal coordinates). /result_data: Contains the curated results used to generate the paper's tables and figures. Terrain Data Files (/terrain_data) For each simulated map (numbered 1 to 50), the following files are available. The number in the filename indicates the map index. Z[map_number].mat: Content: Contains the variable Z. Description: A 2D matrix of elevation values. The matrix size corresponds to a 6000m × 6000m area with a grid resolution defined by 2048 random convex areas. Units: meters. x0_node_num[map_number].mat: Content: Contains the variable x0. Description: A matrix of initial checkpoint coordinates used as a starting point for the optimization algorithms. Each row represents a checkpoint's [x, y] coordinate. Units: meters. Z_diff[map_number].mat: Content: Contains the variable Z_diff. Description: Terrain gradient magnitude (steepness) derived from the elevation data. Z_diff_uniformization[map_number].mat: Content: Contains the variable Z_diff_unif. Description: Normalized terrain gradient, scaled to a range of 0 to 1, where 0 is flat and 1 is the steepest point. diff[map_number].mat: Description: Additional terrain derivative data used in path planning calculations. Results Files (/code_results and /result_data) Results files follow a consistent naming convention: [data_type]_[algorithm][map_number].mat. Data Types: curve: Convergence curve data. fitness: Final objective function value. x: Optimal checkpoint coordinates. Algorithms: DBO (Dung Beetle Optimizer), PSO (Particle Swarm Optimization), SCSO (Sand Cat Swarm Optimization). curve_[algorithm][map_number].mat: Content: Contains the variable convergence_curve. Description: A vector of length 30 (default max iterations). It records the objective function value at each iteration, showing the optimization progress. fitness_[algorithm][map_number].mat: Content: Contains the variable best_fitness. Description: A single scalar value representing the final objective function value (Equation 11 in the paper). Lower values indicate a better route design, balancing fairness and participant experience. x_[algorithm][map_number].mat: Content: Contains the variable best_pos. Description: A matrix of the optimal checkpoint coordinates found by the algorithm. With 5 routes and 6 checkpoints per route, the matrix has 30 rows. The first 6 rows are the coordinates for Route 1, the next 6 for Route 2, and so on. Columns are [x, y] coordinates. Units: meters. Code/software Software Requirements MATLAB Version: R2024b (tested; may work on earlier versions). Operating System: Tested on Windows 10/11. Required Toolboxes: MATLAB Core is sufficient. Optimization Toolbox and Parallel Computing Toolbox are optional but can enhance performance. Code Description All source code is located in the /code folder. Key files include: main.m: The main entry point to run the optimization. Executing this script will run the SCSO algorithm on the default map (Map 1) using Z.mat and x0_node_num.mat. fobj_8.m: Implements the multi-objective function based on the five design principles. SCSO.m, PSO.m, DBO.m: Implementations of the three optimization algorithms. APF_3.m: The Artificial Potential Field method for calculating realistic path lengths between checkpoints, accounting for terrain obstacles. Running the Code Launch MATLAB and navigate to the /code folder. In the command window, type: matlab main To run a different algorithm, edit main.m and uncomment the corresponding line (e.g., % [best_pos, best_fitness, convergence_curve] = PSO(...);). To run on a different map, load the corresponding terrain files before running main: matlab load('../terrain_data/terrain_data1/Z5.mat'); load('../terrain_data/terrain_data1/x0_node_num5.mat'); Access information Other publicly accessible locations of the data: Not applicable Data was derived from the following sources: All data were generated via MATLAB simulations for this study and are not derived from any external sources.README: 面向公平性与沉浸感的户外定向运动——基于增强型沙猫群优化算法的人工智能多目标路径规划框架 数据与文件结构说明 本数据集包含为研究《面向公平性与沉浸感的户外定向运动》生成的所有MATLAB源代码、模拟地形数据与优化结果。所有数据通过MATLAB R2024b进行计算仿真生成。研究通过叠加2048个随机放置的凸区域，生成了50张尺寸为6000m×6000m的随机地形地图，以模拟真实地形起伏。本代码实现了定向运动路径设计（Orienteering Route Design, ORD）的五项设计原则，并采用沙猫群优化算法（Sand Cat Swarm Optimization, SCSO）求解多目标优化问题，同时与粒子群优化算法（Particle Swarm Optimization, PSO）以及蜣螂优化算法（Dung Beetle Optimizer, DBO）进行对比。检查点之间的路径长度通过人工势场法（Artificial Potential Field, APF）计算，以纳入地形障碍物的影响。 文件与变量 文件：Supporting_Information_files.zip 说明： 文件结构与说明 本数据集按以下文件夹组织： /code：包含所有MATLAB源代码。 /terrain_data：包含所有模拟地形数据，分为terrain_data1至terrain_data4子文件夹（对应地图1-50）。 /code_results：包含优化运行的所有原始输出结果（收敛曲线、适应度值、最优坐标）。 /result_data：包含用于生成论文图表与表格的整理后结果。 地形数据文件（/terrain_data） 对于每张编号1至50的模拟地图，提供以下文件。文件名中的数字代表地图索引。 Z[map_number].mat： 内容：包含变量Z。 说明：二维高程值矩阵，矩阵尺寸对应6000m×6000m区域，网格分辨率由2048个随机凸区域定义。 单位：米。 x0_node_num[map_number].mat： 内容：包含变量x0。 说明：初始检查点坐标矩阵，用作优化算法的起点。每行代表一个检查点的[x, y]坐标。 单位：米。 Z_diff[map_number].mat： 内容：包含变量Z_diff。 说明：从高程数据推导得到的地形坡度幅值（陡峭程度）。 Z_diff_uniformization[map_number].mat： 内容：包含变量Z_diff_unif。 说明：归一化地形坡度，缩放至0至1范围，其中0代表平坦区域，1代表最陡峭点。 diff[map_number].mat： 说明：路径规划计算中使用的额外地形导数数据。 结果文件（/code_results与/result_data） 结果文件遵循统一命名规范：[data_type]_[algorithm][map_number].mat。 数据类型： curve：收敛曲线数据。 fitness：最终目标函数值。 x：最优检查点坐标。 算法：DBO（蜣螂优化算法，Dung Beetle Optimizer）、PSO（粒子群优化算法，Particle Swarm Optimization）、SCSO（沙猫群优化算法，Sand Cat Swarm Optimization）。 curve_[algorithm][map_number].mat： 内容：包含变量convergence_curve。 说明：长度为30的向量（默认最大迭代次数），记录每次迭代的目标函数值，展示优化进度。 fitness_[algorithm][map_number].mat： 内容：包含变量best_fitness。 说明：单个标量值，代表最终目标函数值（见论文式11）。值越低表示路径设计越优，可平衡公平性与参与者体验。 x_[algorithm][map_number].mat： 内容：包含变量best_pos。 说明：算法找到的最优检查点坐标矩阵。每条路径包含6个检查点，共5条路径，因此矩阵共有30行。前6行对应路线1的坐标，接下来6行对应路线2，依此类推。列代表[x, y]坐标。 单位：米。 代码与软件 软件要求 MATLAB版本：R2024b（已测试；早期版本或可兼容）。 操作系统：已在Windows 10/11上测试。 所需工具箱：仅需MATLAB核心工具箱。优化工具箱与并行计算工具箱为可选，但可提升运行性能。 代码说明 所有源代码位于/code文件夹中。关键文件包括： main.m：运行优化的主入口点。执行该脚本将在默认地图（地图1）上运行SCSO算法，使用Z.mat与x0_node_num.mat数据。 fobj_8.m：基于五项设计原则实现的多目标函数。 SCSO.m、PSO.m、DBO.m：三种优化算法的实现代码。 APF_3.m：用于计算检查点间真实路径长度的人工势场法代码，可纳入地形障碍物的影响。 代码运行方法 1. 启动MATLAB并导航至/code文件夹。 2. 在命令窗口中输入：main 3. 若需更换算法，编辑main.m并取消对应行的注释（例如：% [best_pos, best_fitness, convergence_curve] = PSO(...);）。 4. 若需在其他地图上运行，在运行main前加载对应地形文件： load('../terrain_data/terrain_data1/Z5.mat'); load('../terrain_data/terrain_data1/x0_node_num5.mat'); 获取途径 本数据无其他公开可用来源。 数据来源 本研究所有数据均通过MATLAB仿真生成，未使用任何外部数据源。