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Multi AUV Intelligent Autonomous Learning Mechanism Based on QPSO Algorithm

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

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Abstract

Intelligent autonomous learning is a hotspot of research on multi-agent system research, because of the PSO algorithm and multiple AUV system has loose coupling intelligent group structure, multiple AUV system flexibility and openness, multiple AUV system can be combined with swarm intelligence algorithm. So, using the swarm intelligence research of particle swarm optimization (pso) autonomous intelligent AUV underwater robot autonomous learning mechanism, can greatly improve the performance of many of AUV system. Quantum PSO algorithm and PSO algorithm based on the experimental verification, to prove QPSO algorithm has the certain superiority in the AUV more autonomous learning, in the process of each iteration self-learning optimal state, not only improve the efficiency of the algorithm, but also for the current to search for the optimal state, improve the accuracy of search algorithm.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (60975071, 61100005), Ministry of Education, Scientific Research Project (13YJA790123), Heilongjiang Province Natural Science Foundation Project (F201425).

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Jian-Jun Li & Ru-Bo Zhang

  2. College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian, 116600, Liaoning, China

    Ru-Bo Zhang

  3. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, 150028, China

    Jian-Jun Li & Yu Yang

Authors
  1. Jian-Jun Li
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  2. Ru-Bo Zhang
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  3. Yu Yang
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Corresponding author

Correspondence to Jian-Jun Li .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Li, JJ., Zhang, RB., Yang, Y. (2015). Multi AUV Intelligent Autonomous Learning Mechanism Based on QPSO Algorithm. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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