As the traditional heuristic algorithm cannot solve the problem of multi-UAVs coordinated global target allocation perfectly, it is difficult to find a reliable initial allocation scheme and the convergence rate is no...
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As the traditional heuristic algorithm cannot solve the problem of multi-UAVs coordinated global target allocation perfectly, it is difficult to find a reliable initial allocation scheme and the convergence rate is not ideal. In order to solve these problems, a multi-UAVs coordinated global target allocation method based on discrete sheep optimization algorithm is proposed. Firstly, with the typical multi-UAVs coordinated attack task as the research background, the UAVs' fuel consumption cost, damage cost and revenue cost are taken as optimization indicators. Meanwhile, the target allocation model is established by considering the flight distance, flight time, load size, and target execution order of the UAVs. Then the penalty function method is used to deal with partially restrained condition to build a fitness function and the discrete sheep optimization algorithm which is discretized and combined with genetic algorithms is used as well to solve the problem. Finally, the simulation results show that the discrete sheep optimization algorithm for multi-UAVs coordinated global target allocation has the advantages of faster convergence rate and better stability compared with the genetic algorithm, and the allocation scheme with lower integrate-cost can be obtained in different scenarios, which can better solve the problem of multi-UAVs coordinated global target allocation.
With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positionin...
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ISBN:
(纸本)9781728173276
With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positioning algorithm with low positioning accuracy and large errors. Aiming at above problems, 3D DV-Hop localization based on improved lion swarm optimization(ILSO) algorithm is proposed. The wolf swarm hunting idea of gray wolf optimizationalgorithm and the herd interaction idea of sheep optimization algorithm are used to improve the lion swarm optimizationalgorithm. The ILSO algorithm is compared with several algorithms and performs well. Then it is applied to the optimization of unknown node coordinates. Simulation results show that the proposed algorithm has higher positioning accuracy than classic 3D DV-Hop algorithm and the 3D DV-Hop algorithm based on the original lion swarm optimizationalgorithm.
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