3 edition of **Fault-tolerance of a neural network solving the Traveling Salesman Problem** found in the catalog.

Fault-tolerance of a neural network solving the Traveling Salesman Problem

- 271 Want to read
- 8 Currently reading

Published
**1989**
by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service [distributor] in Hampton, Va, [Springfield, Va
.

Written in English

- Neural networks (Computer science),
- Fault-tolerant computing.

**Edition Notes**

Other titles | Fault tolerance of a neural network .... |

Statement | P. Protzel, D. Palumbo, M. Arras. |

Series | NASA contractor report -- 181798., ICASE report -- 89-12., NASA contractor report -- NASA CR-181798., ICASE report -- no. 89-12. |

Contributions | Palumbo, D., Arras, Michael K., Langley Research Center. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL18057620M |

1. Introduction. A generalization of the well-known traveling salesman problem (TSP) is the multiple traveling salesman problem (mTSP), which consists of determining a set of routes for m salesmen who all start from and turn back to a home city (depot). Although the TSP has received a great deal of attention, the research on the mTSP is limited. One of the NP-hard routing problems is the Traveling Salesman Problem (TSP). In combinatorial optimization, TSP has been an early proving ground for many approaches, including more recent variants of local optimization techniques such as simulated annealing, tabu search, neural networks.

This paper introduces a new heuristic based on Kohonen's self-organizing feature map for the traveling salesman problem with backhauls (TSPB). The TSPB is an extension of the traveling salesman problem in which a set of customers is partitioned into a set of linehaul customers to be visited contiguously at the beginning of the route and a set of backhaul customers to be visited once all. The traveling salesman problem (TSP) is a classical combinatorial optimization problem of operations research's area, which is simple to state. However, this problem is known to be NP-hard, and cannot be solved exactly in polynomial time. Therefore, it would seem to be an ideal candidate for nonstandard algorithmic approaches, such as natural computation.

This repository is one of three parts that combines into one bigger project that resolves Travelling Salesman Problem using recurrent neural network. Technologies. Used technologies: Python ; PyBrain; Usage. Make sure that you're using Python and you've got PyBrain installed. To train neural network run script: python3 -m main. This paper investigates the fault-tolerance characteristics of time-continuous, recurrent ANN's that can be used to solve optimization problems. The principle of operation and the performance of these networks are first il lustrated by using wel l- known model problems like the traveling salesman prob lem and the assignment problem.

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The Traveling Salesman Problem: A Neural Network Perspective Jean-Yves Potvin Centre de Recherche sur les Transports Université de Montréal C.P.Succ. A, Montréal (Québec) Canada H3C 3J7 [email protected] Abstract. This paper surveys the "neurally" inspired problem-solving approaches to the traveling salesman problem, namely, the.

Get this from a library. Fault-tolerance of a neural network solving the Traveling Salesman Problem. [P Protzel; D Palumbo; M Arras; Langley Research Center.]. This study presents the results of a fault-injection experiment that stimulates a neural network solving the Traveling Salesman Problem (TSP).

The network is based on a modified version of Hopfield's and Tank's original method. We define a performance characteristic for the TSP that allows an overall assessment of the solution quality for different city-distributions and problem : P.

Protzel, D. Palumbo and M. Arras. Traveling salesman problem using neural network techniques Abstract: We discuss two methods for solving the traveling salesman problem (TSP). First, ant system (Ant colony system (ACS)). Second, Neural Network (Hopfield Neural Network). In ACS, a set of cooperating agents called ants cooperate to find good solutions to by: 7.

SOLVING THE TRAVELLING SALESMAN PROBLEM WITH A HOPFIELD-TYPE NEURAL NETWORK 1. Introduction Hopfield-type neural networks [5] composed of highly-interconnected analog elements (neurons) can be successfully used in solving optimization problems. Structure of a network and weights of connections between neu.

network. The principle of how to solve an optimization problem by "mapping" it onto the network is explained in section 3 for two example problems, the assignment problem (AP) and the traveling salesman problem (TSP). Readers who are already familiar with the operation of.

a neural network solving the Traveling Salesman Problem (TSP). The network is based on a modified version of Hopfield's and Tank's original method.

We define a performance characteristic for the TSP that allows an overall assess- ment of the solution quality for different city-distributions and problem.

Abstract: The traveling salesman problem (TSP) is a well known and important combinatorial optimization problem. Taking convex hull as a tool to find the shortest cycle to a TSP, this paper divides vertices representing cities into some nested Hamiltonian cycles without intersection and a residual set of vertices, presents a simple algorithm called Convex Partition and Gradual Insertion.

The Travelling Salesman Problem (TSP) is one of the well-known problems in combinatorial optimization and many researchers have tried to solve this problem with different schemes so far.

In the TSP problem, the objective is on ﬁnding the shortest path between a set of n randomly located cities in which each city is visited only once [1,2]. The traveling salesman problem can be divided into two types: the problems where there is a path between every pair of distinct vertices (no road blocks), and the ones where there are not (with road blocks).

Both of these types of TSP problems are explained in more detail in Chapter 6. Moreover, following the popularity and efficiency of Neural Networks in obtaining good solutions to the Travelling Salesman’s Problem, the authors compared the performance of African Buffalo Optimization to the known solutions of some popular Neural Network algorithms.

These are Angeniol’s method, Somhom et al.’s method, Pasti and Castro. The new scheme has been used to solve the Travelling Salesman Problem (TSP). Experimental results for problems taken from TSPLIB [13] indicate that it is a very accurate NN strategy for the TSP — probably the most accurate neural solutions available in the literature.

We discuss two methods for solving the traveling salesman problem (TSP). First, ant system (Ant colony system (ACS)). Second, Neural Network (Hopfield Neural Network). In ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using indirect form of communication mediated by pheromone they deposit on the edges of TSP graph while.

This paper surveys the “neurally” inspired problem-solving approaches to the traveling salesman problem, namely, the Hopfield-Tank network, the elastic net, and the self-organizing map. A Recurrent Neural Network to Traveling Salesman Problem Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme Niche Pseudo-Parallel Genetic Algorithms for Path Optimization of Autonomous Mobile Robot – A Specific Application of TSP.

This paper surveys the “neurally” inspired problem-solving approaches to the traveling salesman problem, namely, the Hopfield-Tank network, the elastic net, and the self-organizing map.

The latest achievements in the neural network domain are reported and numerical comparisons are provided with the classical solution approaches of. Introduction. Several industrial applications such as Network routing, IOT network routing, Robotics, Path planning [24, 31] are typical combinatorial optimization problems for which the Traveling Salesman Problem, TSP, could serve as a testis a popular optimization problem in which a salesman has to visit a set of predefined cities and return to his first position without.

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem.

It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Moreover, following the popularity and efficiency of Neural Networks in obtaining good solutions to the Travelling Salesman's Problem, the authors compared the performance of African Buffalo Optimization to the known solutions of some popular Neural Network algorithms.

These are Angeniol's method, Somhom et al.'s method, Pasti and Castro's. Skubalska-Rafajłowicz E. () Exploring the Solution Space of the Euclidean Traveling Salesman Problem Using a Kohonen SOM Neural Network. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L., Zurada J.

(eds) Artificial Intelligence and Soft Computing. Assignment Problem (AP) and Traveling Salesman Problem (TSP). With a set of appropriate choices for the parameters in Wang s Recurrent Neural Network, this technique appears to be efficient in solving the mentioned problems in real time.

In cases of solutions that are very close to each other or multiple optimal solutions to.$\begingroup$ I believe that it will be possible for neural networks to solve within a confidence interval with some consistency.

As in a top 5% solution 85% of the time, I was just curious to learn how this sort of problem was solved with a neural network, because I just read the deepmind paper on Neural seems like Neural Networks, especially Deep Reinforcement learning networks.For the traveling salesman problem (TSP) which is also an important aspect for mobile robots, a continuous Hopfield neural network based on dynamic step is applied to solve TSP.