Abstract
This article proposes a novel crossover operator of hybrid genetic algorithms (HGAs) with a Lin-Kernighan (LK) heuristic for solving large-scale traveling salesman problems (TSPs). The proposed crossover, tentatively named sub-tour recombination crossover (SRX), collects many short sub-tours from both parents under some set of rules, and reconnects them to construct a new tour of the TSP. The method is evaluated from the viewpoint of tour quality and CPU time for ten well-known benchmarks, e.g., dj38, qa194, …, ch71009.tsp, in the TSP website of the Georgia Institute of Technology. We compare the SRX with three conventional crossover operators, a variant of the maximal preservative crossover operator (MPX3), a variant of the greedy sub-tour crossover operator (GSX2), and a variant of the edge recombination crossover operator (ERX6), and show that the SRX succeeded in finding a better solution and running faster than the conventional methods mentioned above.
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This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010
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Kuroda, M., Yamamori, K., Munetomo, M. et al. Development of a novel crossover of hybrid genetic algorithms for large-scale traveling salesman problems. Artif Life Robotics 15, 547–550 (2010). https://doi.org/10.1007/s10015-010-0866-8
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DOI: https://doi.org/10.1007/s10015-010-0866-8