基于贪心算法的个性化旅游路线规划，兼顾效率与游客需求。
Research on Travel Route Planing Problems Based on Greedy Algorithm
- 用主成分分析降维并结合KMO、TOPSIS评估城市指标。
- 对不通过KMO检验的数据采用熵权-TOPSIS综合评分。
- 贪心算法优化路线，考虑停留时间与休息，避免局部最优。
基于贪心算法的路径规划是一种在给定起点和终点之间寻找最优或近似最优路径的方法。本文首先利用主成分分析（PCA）对城市评价指标进行降维，提取关键主成分，并基于MindSpore框架，结合KMO和TOPSIS算法对数据进行降维处理。对于未通过KMO检验的数据集，采用熵权法与TOPSIS方法进行综合评价。最后，提出并优化了一种基于贪心算法的路径规划算法，可根据游客的不同需求实现个性化路线定制。同时，算法考虑了本地出行效率、景点访问时间及每日必要休息时间，以降低旅行成本并避免陷入局部最优解。
原文摘要 · Abstract (English)
The route planning problem based on the greedy algorithm represents a method of identifying the optimal or near-optimal route between a given start point and end point. In this paper, the PCA method is employed initially to downscale the city evaluation indexes, extract the key principal components, and then downscale the data using the KMO and TOPSIS algorithms, all of which are based on the MindSpore framework. Secondly, for the dataset that does not pass the KMO test, the entropy weight method and TOPSIS method will be employed for comprehensive evaluation. Finally, a route planning algorithm is proposed and optimised based on the greedy algorithm, which provides personalised route customisation according to the different needs of tourists. In addition, the local travelling efficiency, the time required to visit tourist attractions and the necessary daily breaks are considered in order to reduce the cost and avoid falling into the locally optimal solution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。