Open AccessMulti-UAV Task Assignment Based on Quantum Genetic Algorithm
Author(s) -
Yang Wang,
Xin Yan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1824/1/012010
Subject(s) - computer science , task (project management) , resource allocation , genetic algorithm , simulated annealing , mathematical optimization , jump , population , distributed computing , algorithm , machine learning , engineering , mathematics , computer network , physics , demography , systems engineering , quantum mechanics , sociology
Multi UAV cooperation is an important application of multi UAV cooperation to complete complex tasks. In the aspect of multi UAV system coordination consists of some problems such as difficult to describe complex tasks, difficult to allocate load balancing, and difficult to model tasks. Therefore, making full use of all UAV resources and reasonable task modeling and task allocation to minimize the resource consumption of UAV system is the core problem of multi UAV cooperation. Task allocation is one of the important links of UAV cooperation, which has an important impact on the overall combat effectiveness of the system. This paper establishes the optimization model of multi UAV cooperative task allocation, and then designed a hybrid task allocation method. Quantum genetic algorithm is used for global task allocation in the initial state, and the grouping optimization strategy of hybrid frog leaping algorithm is used to greatly reduce the overall iteration times of the algorithm; the simulated annealing criterion is used to accept new solutions, which can better maintain the diversity of the population and help to jump out of the local extremum.