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Evolutionary-based automatic clustering method for optimizing multilevel network

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Abstract

Route calculation is a problem in real life of every day. Multilevel road network is an effective way to speed up the optimal route calculation on large size road networks by separating the original road network into subnetworks and pre-computing new sections on the higher level network. The efficiency of the route calculation depend on how to separating the original road networks. In order to optimize the structure of multilevel networks, an evolutionary-based automatic clustering method, which can automatically search for a proper number as the number of clusters, is proposed to sperate the road network in this paper. Geographic based crossover and differential evolution based mutation are used to enhance the searching ability of the proposed method. Two objectives are considered during the evolutionary process. A practical evaluation is designed to select a proper solution from the Pareto solution set. The proposed algorithm is systematically studied in various sizes of road networks. Experimental results show that the multilevel networks constructed by the proposed approach are effective and efficient for route calculation.

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Notes

  1. http://www.dis.uniroma1.it/challenge9/data/tiger/.

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Acknowledgements

This research was partially funded by the National Natural Science Foundation of China (Grant No. 61672359), Opening Funding of Key Subject of Computer Application Technology Laboratory in Shenyang Ligong University and supported by CERNET Innovation Project (Grant No. NGII20161005).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China

    Feng Wen, Xingqiao Wang & Guo Zhang

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  1. Feng Wen
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  2. Xingqiao Wang
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  3. Guo Zhang
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Correspondence to Feng Wen.

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Wen, F., Wang, X. & Zhang, G. Evolutionary-based automatic clustering method for optimizing multilevel network. Cluster Comput 20, 3161–3172 (2017). https://doi.org/10.1007/s10586-017-1030-1

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