Volume 14, Issue 1, 2021, Pages 159 - 167
A Novel Target Searching Algorithm for Swarm UAVs Inspired From Spatial Distribution Patterns of Plant Population
Authors
Xiaoming Zhang1, 2, , Yongqiang Hu3, , Tingjuan Li3, *,
1Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, China
2Peng Cheng Laboratory, No. 2 Xingke First Street, Shenzhen, 518066, China
3Qinghai Institute of Science and Technology Information, 4 Xinning Road, Xining, 810008, China
*Corresponding author. Email: litingjuan111@163.com
Corresponding Author
Tingjuan Li
Received 14 August 2020, Accepted 3 November 2020, Available Online 17 November 2020.
- DOI
- 10.2991/ijcis.d.201109.001How to use a DOI?
- Keywords
- Swarm robotics; Target search; Swarm intelligence; Bean optimization algorithm; Cauchy distribution
- Abstract
Like some social animal groups, the evolution of plant populations in nature also contains swarm intelligence. Aiming at the problem of swarm Unmanned Aerial Vehicles (UAVs) target searching in complex and unknown environments, this paper explores search models suitable for swarm UAVs by investigating the spatial distribution patterns of plant population and proposes a novel robot bean optimization algorithm (RBOA) based on Cauchy distribution and normal distribution (CRBOA). It has been demonstrated by comparative experiments with RBOA and adaptive robotic particle swarm optimization (A-RPSO) that the CRBOA has excellent stability, striking a good balance between exploration and exploitation.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Xiaoming Zhang AU - Yongqiang Hu AU - Tingjuan Li PY - 2020 DA - 2020/11/17 TI - A Novel Target Searching Algorithm for Swarm UAVs Inspired From Spatial Distribution Patterns of Plant Population JO - International Journal of Computational Intelligence Systems SP - 159 EP - 167 VL - 14 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.201109.001 DO - 10.2991/ijcis.d.201109.001 ID - Zhang2020 ER -