A new genetic algorithm for the asymmetric traveling salesman problem

► This paper presents a genetic algorithm for solving the asymmetric traveling salesman problem. ► Experimental results demonstrate that the proposed GA outperforms state-of-the-art heuristic algorithms. ► Effective crossover operator for the ATSP is designed. ► We provide useful insights for design...

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Bibliographic Details
Published in:Expert systems with applications 2012-08, Vol.39 (10), p.8947-8953
Main Authors: Nagata, Yuichi, Soler, David
Format: Article
Language:English
Subjects:
Asymmetric traveling salesman problem
Asymmetry
Benchmarking
Combinatorial analysis
Crossover operator
Expert systems
Genetic algorithm
Genetic algorithms
Metaheuristics
Optimization
Transformations
Traveling salesman problem
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Summary:► This paper presents a genetic algorithm for solving the asymmetric traveling salesman problem. ► Experimental results demonstrate that the proposed GA outperforms state-of-the-art heuristic algorithms. ► Effective crossover operator for the ATSP is designed. ► We provide useful insights for designing effective GAs. ► We create a new set of benchmarks for the ATSP. The asymmetric traveling salesman problem (ATSP) is one of the most important combinatorial optimization problems. It allows us to solve, either directly or through a transformation, many real-world problems. We present in this paper a new competitive genetic algorithm to solve this problem. This algorithm has been checked on a set of 153 benchmark instances with known optimal solution and it outperforms the results obtained with previous ATSP heuristic methods.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.02.029