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Open AccessArticle

Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

by 1,2, 1,2,3,4,* and 1,3
1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
3
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
4
Beijing Laboratory for Urban Mass Transit, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2822; https://doi.org/10.3390/app10082822
Received: 27 February 2020 / Revised: 30 March 2020 / Accepted: 15 April 2020 / Published: 19 April 2020
(This article belongs to the Special Issue Advances in Robotics-Based Automation Systems)
The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy. View Full-Text
Keywords: multiple unmanned aerial vehicles (multi-UAVs); fruit fly optimization algorithm (FOA); path planning; multi-swarms multiple unmanned aerial vehicles (multi-UAVs); fruit fly optimization algorithm (FOA); path planning; multi-swarms
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Shi, K.; Zhang, X.; Xia, S. Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs. Appl. Sci. 2020, 10, 2822.

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