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An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

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  • Yong Deng
  • Yang Liu
  • Deyun Zhou

Abstract

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on the k -means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process is -means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

Suggested Citation

  • Yong Deng & Yang Liu & Deyun Zhou, 2015. "An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-6, October.
    • Handle: RePEc:hin:jnlmpe:212794
    DOI: 10.1155/2015/212794
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    Cited by:

    1. Lei Chen & Ling Diao & Jun Sang, 2019. "A novel weighted evidence combination rule based on improved entropy function with a diagnosis application," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    2. Ravindra Kumar & Rajeev Kumar Mishra & Satish Chandra & Asif Hussain, 2021. "Evaluation of urban transport-environment sustainable indicators during Odd–Even scheme in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17240-17262, December.
    3. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
    4. Shardrom Johnson & Jinwu Han & Yuanchen Liu & Li Chen & Xinlin Wu, 2018. "Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling," Future Internet, MDPI, vol. 10(8), pages 1-15, July.
    5. Liu, Yang & Wei, Bo & Du, Yuxian & Xiao, Fuyuan & Deng, Yong, 2016. "Identifying influential spreaders by weight degree centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 1-7.

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