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An Improved Genetic Algorithm Based on Gene Pool for TSP

  • Conference paper
Pervasive Computing and the Networked World (ICPCA/SWS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 8351))

Abstract

Traveling salesman problem is a typical representative of combinatorial optimization problems. An improved Genetic algorithm is proposed for solving Traveling Salesman Problem (TSP). This Partheno-genetic algorithm employs only mutation and selection operators to produce the offspring, A new combinatory operator is designed combining the gene pool operator with inversion operator which ensures its strong searching capability. The gene pool directs the single-parent evolution and enhances the evolutionary speed. This algorithm simulates the recurrence of nature evolution process. Experiments based on 4 instances selected from TSPLIB are used to test the performance of this algorithm. They prove that it can reach the satisfying optimization at a faster speed. Especially, for the KroA100, the best path it finds is better than any other available one.

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Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Shandong Normal University, Jinan Shandong, China

    Jianping Zhang & Xiyu Liu

Authors
  1. Jianping Zhang
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  2. Xiyu Liu
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Editor information

Editors and Affiliations

  1. School of Logistics Engineering, Wuhan University of Technology, 430063, Wuhan, Hubei, China

    Qiaohong Zu

  2. Facultad de Ingenieria y Ciencias Universidad Adolfo Ibanez, Vina del Mar, Chile

    Maria Vargas-Vera

  3. Fujitsu, Hayes, Middlesex, UK

    Bo Hu

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© 2014 Springer International Publishing Switzerland

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Zhang, J., Liu, X. (2014). An Improved Genetic Algorithm Based on Gene Pool for TSP. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2013. Lecture Notes in Computer Science, vol 8351. Springer, Cham. https://doi.org/10.1007/978-3-319-09265-2_78

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  • DOI: https://doi.org/10.1007/978-3-319-09265-2_78

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09264-5

  • Online ISBN: 978-3-319-09265-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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