DocumentCode :
1557917
Title :
A hybrid heuristic for the traveling salesman problem
Author :
Baraglia, R. ; Hidalgo, J.I. ; Perego, R.
Author_Institution :
Ist. CNUCE, CNR, Pisa, Italy
Volume :
5
Issue :
6
fYear :
2001
fDate :
12/1/2001 12:00:00 AM
Firstpage :
613
Lastpage :
622
Abstract :
The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. The local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13,509 cities show the efficacy of the symbiosis between the two heuristics
Keywords :
genetic algorithms; search problems; travelling salesman problems; Lin-Kernighan algorithm; compact genetic algorithms; heuristics; local search; traveling salesman problem; Cities and towns; Genetic algorithms; Helium; Heuristic algorithms; Hybrid power systems; Optimization methods; Symbiosis; Taxonomy; Testing; Traveling salesman problems;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/4235.974843
Filename :
974843
Link To Document :
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