Summary of Research on Travel Route Planing Problems Based on Greedy Algorithm, by Yiquan Wang
Research on Travel Route Planing Problems Based on Greedy Algorithm
by Yiquan Wang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to solving the route planning problem using a greedy algorithm. By employing PCA, KMO, and TOPSIS methods based on the MindSpore framework, the authors aim to downscale city evaluation indexes and extract key principal components. A comprehensive evaluation is conducted for datasets that don’t pass the KMO test, utilizing entropy weights and TOPSIS methods. The proposed route planning algorithm optimizes greedy routing by considering local travel efficiency, time required to visit attractions, and daily breaks. This personalized approach reduces costs and avoids locally optimal solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem called route planning using a new way of thinking. It’s like finding the best path from one place to another. The authors use special math tools (PCA, KMO, and TOPSIS) to make the city evaluation indexes smaller and focus on what’s most important. For some datasets that don’t work well with these tools, they use different methods to get a better answer. They then create an algorithm that helps people find the best route for their needs, considering things like how long it takes to visit places and when to take breaks. This makes it more efficient and cost-effective. |
Keywords
» Artificial intelligence » Pca
