Unmanned aerial vehicle cluster search control optimization method based on tabu table improved sparrow algorithm
An optimization method and unmanned aerial vehicle technology, applied in the direction of non-electric variable control, control/regulation system, three-dimensional position/channel control, etc., can solve the problems of low food reserves and inability to overcome the search ability of swarm intelligence optimization algorithm to jump out of local optimum Operation, poor foraging position and other problems, to achieve the effect of improving speed and accuracy, improving search stability and accuracy, and improving stability and accuracy
Pending Publication Date: 2022-03-18
HANGZHOU DIANZI UNIV
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[0009] 3. The identities of discoverers and followers change dynamically
[0010] 4. The lower the food reserves of followers, the worse their foraging position in the whole population
[0013] Compared with the traditional optimization algorithm and the traditional swarm intelligence optimization algorithm, the sparrow algorithm has the advantages of fast convergence speed, high solution accuracy, and strong robustness, but it still cannot overcome the weak global search ability of the swarm intelligence optimization algorithm and the weak operation of jumping out of the local optimum. , easy to fall into local optimum
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[0072] Embodiment: a kind of unmanned aerial vehicle swarm search control optimization method based on taboo table improved sparrow algorithm, in unmanned aerial vehicle swarm coverage search, be divided into discoverer (explorer) and follower (follower), discoverer is in population The middle is responsible for finding the target source and providing the search area and direction for the entire population, while the follower uses the finder to obtain the target. In order to obtain the target, two behavioral strategies, the discoverer and the follower, can usually be used for searching.
[0073] Establish a mathematical model based on the taboo table to improve the sparrow algorithm, the main rules are as follows:
[0074] 1. The discoverer usually has a high energy reserve and is responsible for searching for areas with rich food in the entire population, and provides the area and direction for all followers to forage. In the establishment of the model, the level of energy r...
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The invention discloses an unmanned aerial vehicle cluster search control optimization method based on a tabu table improved sparrow algorithm, and the method specifically comprises the steps: (1) creating a coverage search model to provide a control scheme, (2) initializing a population, generating an unmanned aerial vehicle control scheme, and initializing a tabu table, (3) calculating the proportion of a discoverer to a follower through a self-adaptive updating strategy, and carrying out the distribution, (4) the position of the discoverer is updated according to the improved discoverer updating formula, (5) the position of the follower is updated according to the discoverer updating result and the follower updating formula, and (6) the position of the alerter is updated according to the detection early warning behavior formula. (7) judging the fitness value of the discoverer with the maximum fitness value in the current iteration and the numerical value in the taboo table, and (8) returning to the step (3) for iteration until the iteration requirement is met. According to the method, the cluster search control capability of the unmanned aerial vehicle cluster is improved, and the search stability and accuracy are improved.
Description
technical field [0001] The invention relates to an optimization method for unmanned aerial vehicle cluster search control, in particular to an optimized method for unmanned aerial vehicle cluster search control based on a taboo table improved sparrow algorithm. Background technique [0002] As a type of unmanned aerial vehicle that uses electromagnetic waves to remotely control or operate according to its own program, compared with manned aircraft, UAV has many advantages such as high flexibility, good maneuverability, and no casualties, and is gradually being widely used. in different areas. Compared with a single UAV, UAV swarm control cooperative search has many advantages. First of all, UAV clusters have the advantage of no center. For UAV clusters, each UAV has independent decision-making ability and does not depend on the control of a central UAV node. Therefore, even if some UAVs fail Under the circumstances, the remaining UAVs can still cooperate to complete the se...
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IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 陈滨黄彦博魏丹邵艳利方景龙
Owner HANGZHOU DIANZI UNIV