# Research on Travel Route Planing Problems Based on Greedy Algorithm

arXiv: https://arxiv.org/abs/2410.13226 · HTML: https://mlsanity.com/paper/2410.13226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Octopus Inspired Optimization (OIO): A Hierarchical Framework for Navigating Protein Fitness Landscapes](https://mlsanity.com/paper/2410.07968/) (2024)
- [A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis](https://mlsanity.com/paper/2410.10658/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning](https://mlsanity.com/paper/2506.06121/) (2025)

## Abstract (arXiv)

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.