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Dynamic route guidance algorithm based on artificial immune system

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Abstract

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China

    Licai Yang, Jie Lin & Lei Jia

  2. Network Center, Shandong University, Jinan, Shandong, 250061, China

    Dewei Wang

Authors
  1. Licai Yang
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  2. Jie Lin
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  3. Dewei Wang
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  4. Lei Jia
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Corresponding author

Correspondence to Licai Yang.

Additional information

This work was supported by the Natural Science Foundation of Shandong Province(No.Y2005G12), the National Natural Science Foundation of China(No.60674062) and the Information Industry Foundation of Shandong Province(No.2006R00046).

Licai YANG Received his B.E. degree in automation and the M.E. degree in control engineering from Shandong University of Technology, China, and the Ph.D degree in control theory and control engineering from Shandong University, China. He is currently a professor in Shandong University. His research interests include artificial intelligence and intelligent control, intelligent transportation systems, biomedical engineering, and control theory and applications.

Jie LIN Received her B.E. degree in Information Science & Electronic Engineering from Zhejiang University, China, in 2003. Currently, she is a graduate student pursuing an M.E. degree in control theory and engineering in Shandong University, China. Her research direction is artificial intelligence and intelligent transportation systems.

Dewei WANG Received his B.E. degree in automation and the M.E. degree in control engineering from Shandong University of Technology, China, in 1984 and 1990, respectively. He is currently an associate professor in Shandong University, China. His research interests include artificial intelligence and network security.

Lei JIA Received his B.E. degree in automation and the M.E. degree in control engineering from Shandong University of Technology, China, in 1982 and 1988, respectively, and the Ph.D degree in control theory and control engineering from Zhejiang University, China, in 1991. Since 1995, he has been a professor in Shandong University, China. His research interests include artificial intelligence and intelligent control, robust control, intelligent transportation systems, control theory and applications.

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Yang, L., Lin, J., Wang, D. et al. Dynamic route guidance algorithm based on artificial immune system. J. Control Theory Appl. 5, 385–390 (2007). https://doi.org/10.1007/s11768-006-6112-1

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  • DOI: https://doi.org/10.1007/s11768-006-6112-1

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