Comparison of the Traveling Salesman Problem Analysis Using Neural Network Method
Aeri Rachmad, Eka Mala Sari Rochman, Dwi Kuswanto, Iwan Santosa, Rinci Kembang Hapsari, Tutuk Indriyani, Endah Purwanti
Available Online December 2018.
- https://doi.org/10.2991/icst-18.2018.213How to use a DOI?
- Traveling salesman problem, optimization, Hopfield, neural networks
- Traveling sales problem (TSP) is one of the classical optimization problems and NP-complete. The Challenge in implementing TSP is how to determine the shortest distance from a traveling route of N cities where each N city visited precisely once at time. In this paper, we attempt to analyze the completion of TSP using an artificial neural network approach. In network, weights are determined to represent problem boundaries and optimize the completion function. The approach of artificial neural networks used is a network with Hopfield algorithm and Simulated Annealing algorithm. The solution with the Hopfield algorithm has the complexity of the 4n4 + 16n3 algorithm, while the complexity of the Simulated Annealing algorithm is 10n2. Judging from the use of memory in the implementation, the application of annealing algorithm is better than the Hopfield algorithm. but both are algorithms that "no works in place".
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Aeri Rachmad AU - Eka Mala Sari Rochman AU - Dwi Kuswanto AU - Iwan Santosa AU - Rinci Kembang Hapsari AU - Tutuk Indriyani AU - Endah Purwanti PY - 2018/12 DA - 2018/12 TI - Comparison of the Traveling Salesman Problem Analysis Using Neural Network Method BT - Proceedings of the International Conference on Science and Technology (ICST 2018) PB - Atlantis Press SP - 1057 EP - 1061 SN - 2589-4943 UR - https://doi.org/10.2991/icst-18.2018.213 DO - https://doi.org/10.2991/icst-18.2018.213 ID - Rachmad2018/12 ER -