Comparison Between Greedy And Genetic Algorithm In Travelling Salesman Problem

Chandra, Samuel Stephano (2020) Comparison Between Greedy And Genetic Algorithm In Travelling Salesman Problem. Other thesis, Universitas Katolik Soegijapranata Semarang.

[img] Text (Samuel Stephano Chandra - COVER)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - COVER.pdf
Download (914kB)
[img] Text (Samuel Stephano Chandra - BAB I)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB I.pdf
Download (462kB)
[img] Text (Samuel Stephano Chandra - BAB II)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB II.pdf
Restricted to Registered users only
Download (475kB)
[img] Text (Samuel Stephano Chandra - BAB III)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB III.pdf
Download (463kB)
[img] Text (Samuel Stephano Chandra - BAB IV)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB IV.pdf
Download (2MB)
[img] Text (Samuel Stephano Chandra - BAB V)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB V.pdf
Download (988kB)
[img] Text (Samuel Stephano Chandra - BAB VI)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - BAB VI.pdf
Download (248kB)
[img] Text (Samuel Stephano Chandra - DAFTAR PUSTAKA)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - DAFTAR PUSTAKA.pdf
Download (515kB)
[img] Text (Samuel Stephano Chandra - LAM[IRAN)
16.K1.0004 -SAMUEL STEPHANO CHANDRA - LAMPIRAN.pdf
Download (759kB)

Abstract

This research was made in order to compare branch and bound algorithms and genetics. In this case, the TSP case is taken, which means that it requires the fastest route to get from one point to another without being able to pass the same point for the data used is data from the Polrestabes of Semarang about which areas will be sprayed disinfectant. For the data route, it will be taken directly to the place and via online, then made into a graph. For coordinates and distance are also assisted with Google maps. Then the data is converted into a table form. The final results of this study are, two algorithms used are able to provide optimal results, the fastest route when compared to the initial route, and with some data, both algorithms can show which is better in producing an optimal route. For the 10 datasets scenario, the Greedy Algorithm cannot give good results compared to the initial results, but the Genetic Algorithm is able to give lower results. But on 70 datasets scenario, both algorithms are able to provide optimal results. The Greedy Algorithm is superior in the use of its algorithm because it is simpler than the Genetic Algorithm which has to balance the many factors needed.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: ms F. Dewi Retnowati
Date Deposited: 28 May 2021 02:40
Last Modified: 28 May 2021 02:40
URI: http://repository.unika.ac.id/id/eprint/25278

Actions (login required)

View Item View Item
Unika Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.