A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks

Traveler Salesman Problem (TSP) is one the most famous and important problems in the field of operation research and optimization. This problem is a NP-Hard problem and it is aimed to find a minimum Hamiltonian cycle in a connected and weighed graph. In the last decades, many innovative algorithms have been presented to solve this problem but most of them are inappropriate and inefficient and have high complexity. In this paper, we combined Hopfield neural network with genetic algorithm to solve this problem, and showed that the results of the algorithm are more efficient that the other similar algorithms. General Terms Algorithms

[1]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[2]  Bernd Freisleben,et al.  New Genetic Local Search Operators for the Traveling Salesman Problem , 1996, PPSN.

[3]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[4]  D. E. Goldberg,et al.  Genetic Algorithm in Search , 1989 .

[5]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[6]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[7]  Bruce W. Colletti,et al.  Quasiabelian landscapes of the traveling salesman problem are elementary , 2009, Discret. Optim..

[8]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[9]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[10]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[11]  Richard E. Neapolitan,et al.  Foundations of Algorithms Using C++ Pseudocode , 2003 .

[12]  Gang Feng,et al.  Using Hopfield networks to solve traveling salesman problems based on stable state analysis technique , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[15]  Robert R. Bies,et al.  A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection , 2006, Journal of Pharmacokinetics and Pharmacodynamics.

[16]  Nikbakhsh Javadian,et al.  An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems , 2011, Appl. Soft Comput..

[17]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[18]  Zbigniew Michalewicz,et al.  Design by Evolution , 2008 .