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• W2906304617 abstract "The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms." @default.
• W2906304617 created "2019-01-01" @default.
• W2906304617 creator A5001770391 @default.
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• W2906304617 date "2018-12-25" @default.
• W2906304617 modified "2023-09-29" @default.
• W2906304617 title "Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming" @default.
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• W2906304617 doi "https://doi.org/10.3390/info10010007" @default.
• W2906304617 hasPublicationYear "2018" @default.
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