A Self-Organizing Map Algorithm for the Traveling Salesman Problem

2008 ◽  
Author(s):  
Xinshun Xu ◽  
Zhiping Jia ◽  
Jun Ma ◽  
Jiahai Wang
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
The Traveling Salesman Problem ◽  
Self Organizing

scholarly journals Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem

2021 ◽  
Author(s):  
Joao P. A. Dantas ◽  
Andre N. Costa ◽  
Marcos R. O. A. Maximo ◽  
Takashi Yoneyama
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Self Organizing Map ◽  
The Traveling Salesman Problem ◽  
Map Solution ◽  
Self Organizing

Usando um método aprimorado de Mapa Auto-Organizável, fornecemos soluções abaixo do ideal para o Problema do Caixeiro Viajante. Além disso, empregamos o ajuste de hiperparâmetros para identificar os recursos mais críticos do algoritmo. Todas as melhorias no trabalho de benchmark trouxeram resultados consistentes e podem inspirar esforços futuros para melhorar este algoritmo e aplicá-lo a diferentes problemas.

Local Clustering Organization (LCO) Solving a Large-Scale TSP

2005 ◽  
Vol 17 (5) ◽  
pp. 560-567 ◽  
Author(s):  
Masashi Furukawa ◽  
◽  
Michiko Watanabe ◽  
Yusuke Matsumura ◽  
◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Numerical Experiments ◽  
Large Scale ◽  
The Self ◽  
Traveling Salesman ◽  
Self Organizing Map ◽  
Two Cities ◽  
Local Clustering ◽  
Different Types ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.

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