THE APPLICATION OF ANT COLONY OPTIMIZATION (ACO) ALGORITHM IN THE SHORT RUTE SEARCH FOR COMPLETING TRAVELING PROBLEM (TP)

Mutiawati Mutiawati, Nelly Nelly

Abstract


Traveling in the community has become a lifestyle. Factors that become obstacles in every traveling activity include determining the destination. In this process, the traveler needs a short route. The Ant Colony algorithm is one of the heuristic methods for finding the right solution in a discrete optimization problem. The purpose of this study is to apply the workings of the Ant Colony Optimization algorithm in Traveling Problem. The information needed is information related to a location between cities and route planning for destination locations. The distance calculation is done by summing the initial distance to the end of the trip and calculating the cost of fuel. ACO has been applied to find optimal solutions to the Traveling Salesman Problem, by giving a number of n points. 

 

Keywords: Algorithm Ant Colony Optimization, Travelling Problem


Keywords


Algorithm Ant Colony Optimization; Travelling Problem

References


Abdurrazaq, Muhammad N. (2015). DIDS Using Cooperative Agents Based on Ant Colony Clustering. Journal of ICT Research and Applications. Vol. 8, No. 3, 213 – 233.

Adha, Aidil. (2010). Interactive Map Searching for the Shortest Path Using the Ant Algorithm for Pickup of Goods (Case Study of PT. TIKI Pekanbaru). Final Project Informatics Engineering Department. State Islamic University Sultan Syarif Kasim Pekanbaru.

Boryczka, Urszula. (2008). Ant Clustering Algorithm. Poland: Institute of the Computer Science the University of Silesia.

Ekawati, Rooselyna, dkk. (2019). Students’ Cognitive Processes In Solving Problem Related To The Concept Of Area Conservation. Journal Mathematics Education. Vol. 10, No.1, January 2019, pp. 21-36.

Mindaputra, Eka. (2009). The Use of Ant Colony System Algorithm in Traveling Salesman Problem (TSP) at PT. TEKA Jaya Motor. Mathematics Study Program Thesis. Diponegoro University Semarang.

Nurmala, Nila. (2017). Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming. Journal of ICT Research and Applications. Vol. 11, No. 1, 21 – 37.

Wardy, I.S. (2007). Use of Graphs in Ant Algorithms to do optimization, Informatics Engineering Study Program, ITB. Bandung.




DOI: https://doi.org/10.18269/jpmipa.v24i1.15075

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Jurnal Pengajaran MIPA

JPMIPA http://ejournal.upi.edu/index.php/jpmipa/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam (JPMIPA) or Journal of Mathematics and Science Teaching 

All rights reserverd. pISSN 1412-0917 eISSN 2443-3616

Copyright © Faculty of Mathematics and Science Education (FPMIPA) Universitas Pendidikan Indonesia (UPI)

 

View JPMIPA Stats