Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming
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Global Energy Interconnection Research Institute Co., Ltd., Beijing 102200, China
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Power Systems Artificial Intelligence Joint Laboratory of SGCC, Beijing 102200, China
3
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 230001, China
4
Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company, Shanghai 200437, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(1), 7; https://doi.org/10.3390/info10010007
Received: 22 November 2018
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Revised: 17 December 2018
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Accepted: 21 December 2018
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Published: 25 December 2018
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.
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Keywords:
graph traversal optimization; gene-expression programming; simulated annealing algorithm; traveling-salesman problem
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MDPI and ACS Style
Zhou, A.-H.; Zhu, L.-P.; Hu, B.; Deng, S.; Song, Y.; Qiu, H.; Pan, S. Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming. Information 2019, 10, 7. https://doi.org/10.3390/info10010007
AMA Style
Zhou A-H, Zhu L-P, Hu B, Deng S, Song Y, Qiu H, Pan S. Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming. Information. 2019; 10(1):7. https://doi.org/10.3390/info10010007
Chicago/Turabian StyleZhou, Ai-Hua, Li-Peng Zhu, Bin Hu, Song Deng, Yan Song, Hongbin Qiu, and Sen Pan. 2019. "Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming" Information 10, no. 1: 7. https://doi.org/10.3390/info10010007
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