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Solving TSP Problems with Hybrid Estimation of Distribution Algorithms

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

In this paper, a hybrid Estimation of Distribution Algorithms is proposed to solve traveling salesman problem, and a greedy algorithm is used to improve the quality of the initial population. It sets up aBayes probabilistic model of the TSP. The roulette method is adopted to generate the new population. In order to prevent falling into local optimum, the mutation and limit were proposed to enhance the exploitation ability. At the same time, three new neighborhood search strategies and the second element optimization method are presented to enhance the ability of the local search. The simulation results and comparisons based on benchmarks validate the efficiency of the proposed algorithm.

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Author information

Authors and Affiliations

  1. College of Software Engineering, University of Science and Technology Liaoning, China

    Xiaoxia Zhang & Yunyong Ma

Authors
  1. Xiaoxia Zhang
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  2. Yunyong Ma
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Zhang, X., Ma, Y. (2014). Solving TSP Problems with Hybrid Estimation of Distribution Algorithms. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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