A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems (1996)
| Venue: | In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation |
| Citations: | 61 - 12 self |
BibTeX
@INPROCEEDINGS{Freisleben96agenetic,
author = {Bernd Freisleben and Peter Merz},
title = {A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems},
booktitle = {In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation},
year = {1996},
pages = {616--621},
publisher = {IEEE Press}
}
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Abstract
The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time. I. Introduction In the Traveling Salesman Problem (TSP) [18], [27], a number of cities with distances between them is given and the task is to find the minimum--length closed tour that visits each city once and returns to its starting point. A symmetric TSP (STSP) is one where the distance between any...
