Traveling salesperson problem using Python
A well-known optimization issue in operations research, mathematics, and computer science is the Traveling Salesman Problem (TSP). It entails determining the quickest path a salesman can take to travel to a series of cities, stop in each one exactly once, and then return to the beginning locat...
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/167015 |
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| 總結: | A well-known optimization issue in operations research, mathematics, and computer
science is the Traveling Salesman Problem (TSP). It entails determining the quickest path
a salesman can take to travel to a series of cities, stop in each one exactly once, and then
return to the beginning location. The issue is notoriously challenging because there are so
many potential routes that they increase exponentially with the number of cities, rendering
it unsolvable for a sizable number of cities. The TSP has been solved using a variety of
algorithms, including heuristic and metaheuristic methods like genetic algorithms and
simulated annealing, as well as accurate methods like branch and bound. Numerous
industries, including manufacturing, logistics, and transportation, can use the TSP. By
implementing Graph Neural Network (GNN) into TSP, it helps one to visualize the graph
better as the edges and nodes are labelled with numbers |
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