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A GIS-Based Optimization of ACO in UAV Network

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Genetic and Evolutionary Computing (ICGEC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 834))

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Abstract

UAVs can carry out rescue work in the disaster area. The ant colony algorithm is used to search and rescue work in the disaster area based on GIS. Considering regional priority, an algorithm named priority-PAACO is proposed. Simulations and analysis show the effective of this algorithm.

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References

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Author information

Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, 116620, China

    Weifeng Sun, Yuanxun Xing, Guangqun Ma & Shumiao Yu

Authors
  1. Weifeng Sun
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  2. Yuanxun Xing
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  3. Guangqun Ma
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  4. Shumiao Yu
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Corresponding author

Correspondence to Weifeng Sun .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, China

    Jerry Chun-Wei Lin

  3. Changzhou College of Information Technology, Changzhou, Jiangsu, China

    Bixia Sui

  4. College of Smart Living and Management, Tajen University, Pingtung, Taiwan

    Shih-Pang Tseng

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Sun, W., Xing, Y., Ma, G., Yu, S. (2019). A GIS-Based Optimization of ACO in UAV Network. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_9

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