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Hybrid Ejection Chain Methods for the Traveling Salesman Problem

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

Local search such as Ejection Chain Methods (ECMs) based on the stem-and-cycle (S&C) reference structure, Lin-Kernighan (LK) heuristics, as well as the recently proposed Multi-Neighborhood Search (MNS), are among the most competitive algorithms for the Traveling Salesman Problem (TSP). In this paper, we carry out a large-scale experiment with all 110 symmetric instances from the TSPLib to investigate the performances of these algorithms. Our study is different from previous work along this line of research in that we consider the entire runtime behavior of the algorithms, not just their end results. This leads to one of the most comprehensive comparisons of these algorithms to date. We introduce a new, improved S&C-ECM that can outperform LK and MNS. We then develop new hybrid versions of our ECM implementations by combining them with Evolutionary Algorithms and Population-based Ant Colony Optimization (PACO). We compare them to similar hybrids of LK and MNS. Our results show that hybrid PACO-S&C, PACO-LK and PACO-MNS are all very efficient. We also find that the full runtime behavior comparison provides deeper and clearer insights, while focusing on end results only would have led to a misleading conclusion.

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Acknowledgements

We acknowledge support from the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China No. 61150110488, Special Financial Grant 201104329 from the China Postdoctoral Science Foundation, the Chinese Academy of Sciences (CAS) Fellowship for Young International Scientists 2011Y1GB01, and the European Union 7th Framework Program under Grant 247619. The experiments reported in this paper were executed on the supercomputing system in the Supercomputing Center of University of Science and Technology of China.

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Authors and Affiliations

  1. Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Weichen Liu, Thomas Weise & Yuezhong Wu

  2. School of Design, Communication and IT, Faculty of Science and IT, The University of Newcastle, Callaghan, NSW 2308, Australia

    Raymond Chiong

Authors
  1. Weichen Liu
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  2. Thomas Weise
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  3. Yuezhong Wu
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  4. Raymond Chiong
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Corresponding author

Correspondence to Thomas Weise .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Liu, W., Weise, T., Wu, Y., Chiong, R. (2015). Hybrid Ejection Chain Methods for the Traveling Salesman Problem. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_25

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_25

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  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-662-49014-3

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