Comparison of the Traveling Salesman Problem Analysis Using Neural Network Method

DOI

10.2991/icst-18.2018.213How to use a DOI?

Keywords

Traveling salesman problem, optimization, Hopfield, neural networks

Abstract

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".

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/icst-18.2018.213
ID  - Rachmad2018/12
ER  -