A Framework To Study Heuristic TSP Algorithms With Google Maps API
| dc.contributor.author | Ajumal P.A. | |
| dc.contributor.author | Ananthakrishnan S. | |
| dc.contributor.author | Jain A. | |
| dc.contributor.author | Athreya H.N. | |
| dc.contributor.author | Chandrasekaran K. | |
| dc.date.accessioned | 2021-05-05T10:15:43Z | |
| dc.date.available | 2021-05-05T10:15:43Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Millions of people depend on the navigation facilities available in smart-phones and web browsers for their daily commutes, planning long trips ahead of time, looking up places etc. Integration of GPS and compass made navigating anywhere in the world a trivial task. Today, there are several applications available that fit the purpose of navigation such as Waze, HereWeGo (previously known as Here Maps by Nokia), Google Maps, etc. When Google Maps was used to embark on a tour that will take us to chosen places by covering the least distance possible, it is observed that none of the aforementioned applications provide such a feature. In this paper, a framework is developed with Google Maps APIs to create such a feature. This problem is mapped to the Traveling salesman problem and tried to solve it using algorithms known for approximating TSP such as Artificial Bee Colony Algorithm, Particle Swarm Optimization and Two-opt Algorithm. The framework is tested with these algorithms and found that, Particle Swarm Optimization gives the best possible route. © 2019 IEEE. | en_US |
| dc.identifier.citation | 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2019 - Proceedings , Vol. , , p. - | en_US |
| dc.identifier.uri | https://doi.org/10.1109/DISCOVER47552.2019.9008062 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/14743 | |
| dc.title | A Framework To Study Heuristic TSP Algorithms With Google Maps API | en_US |
| dc.type | Conference Paper | en_US |