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

2005 ◽  
Vol 17 (5) ◽  
pp. 560-567 ◽  
Author(s):  
Masashi Furukawa ◽  
◽  
Michiko Watanabe ◽  
Yusuke Matsumura ◽  
◽  
...  

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.

2018 ◽  
Vol 17 (01) ◽  
pp. 1850003
Author(s):  
Tamás Kalmár-Nagy ◽  
Bendegúz Dezső Bak

We propose a hierarchical heuristic approach for solving the Traveling Salesman Problem (TSP) in the unit square. The points are partitioned with a random dyadic tiling and clusters are formed by the points located in the same tile. Each cluster is represented by its geometrical barycenter and a “coarse” TSP solution is calculated for these barycenters. Midpoints are placed at the middle of each edge in the coarse solution. Near-optimal (or optimal) minimum tours are computed for each cluster. The tours are concatenated using the midpoints yielding a solution for the original TSP. The method is tested on random TSPs (independent, identically distributed points in the unit square) up to 10,000 points as well as on a popular benchmark problem (att532 — coordinates of 532 American cities). Our solutions are 8–13% longer than the optimal ones. We also present an optimization algorithm for the partitioning to improve our solutions. This algorithm further reduces the solution errors (by several percent using 1000 iteration steps). The numerical experiments demonstrate the viability of the approach.


2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Pengzhen Du ◽  
Ning Liu ◽  
Haofeng Zhang ◽  
Jianfeng Lu

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms.


2021 ◽  
Author(s):  
Joao P. A. Dantas ◽  
Andre N. Costa ◽  
Marcos R. O. A. Maximo ◽  
Takashi Yoneyama

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.


2011 ◽  
Vol 74 (5) ◽  
pp. 671-679 ◽  
Author(s):  
Jan Faigl ◽  
Miroslav Kulich ◽  
Vojtěch Vonásek ◽  
Libor Přeučil

2012 ◽  
Vol 198-199 ◽  
pp. 1321-1326 ◽  
Author(s):  
Yu Liu ◽  
Guo Dong Wu

When solving large scale combinatorial optimization problems, Max-Min Ant System requires long computation time. MPI-based Parallel Max-Min Ant System described in this paper can ensure the quality of the solution, as well as reduce the computation time. Numerical experiments on the multi-node cluster system show that when solving the traveling salesman problem, MPI-based Parallel Max-Min Ant System can get better computational efficiency.


2017 ◽  
Vol 5 (2) ◽  
pp. 284-291
Author(s):  
Wafaa Mustafa Hameed ◽  
Asan Baker Kanbar

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used , one of the problems in using genetic algorithms is the choice of crossover operator Many crossover operators have been proposed in literature on evolutionary algorithms, however, it is still unclear which crossover operator works best for a given optimization problem. This paper aims at studying the behavior of different types of crossover operators in the performance of genetic algorithm. These types of crossover are implemented on Traveling Salesman Problem (TSP); Whitley used the order crossover (OX) depending on specific parameters to solve the traveling salesman problem, the aim of this paper is to make a comparative study between order crossover (OX) and other types of crossover using the same parameters which was Whitley used.


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