A Multi-UAV Collaborative Search Method Based on Improved Pigeon Group Optimization

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative search method based on improved pigeon group optimization, which includes: first establishing a search map model, and using a Markov model to establish a target information map, and then establishing a movement model of the unmanned aerial vehicle and digital Pheromone map; apply the pigeon swarm optimization algorithm for multi-UAV collaborative search, and use chaos and reverse strategies to realize the initialization of the population position to ensure the randomness of the initial position; the first stage is to iterate the map and compass operator When using Cauchy mutation to prevent falling into local optimum, in the second stage of iteration of landmark operator, simulated annealing is used to retain some individuals with poor performance and Gaussian mutation to avoid premature falling into local optimum. The present invention combines the use of chaos and reverse strategies to initialize the population position, the mutation introduced in order to avoid falling into local optimum and the simulated annealing algorithm, which solves the dynamic target and repeated search problems in the search process, and effectively improves the search efficiency. efficiency.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle cooperative search method based on improved pigeon group optimization, which belongs to the fields of collaborative search and swarm intelligence optimization. Background technique [0002] Since the 1990s, algorithms obtained by simulating and revealing certain natural phenomena or processes have been developed, such as particle swarm algorithm, ant colony algorithm, and pigeon colony algorithm mentioned in this article. These algorithms have unique advantages and mechanisms, and provide new ideas and means for solving complex problems, and are widely used in many fields. These algorithms are called swarm intelligence optimization algorithms because of their intuition and natural mechanism. [0003] The group in Swarm Intelligence is a group of subjects (Agents) that can communicate directly or indirectly with each other, and these subjects can solve distributed problems through cooperation. S...

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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the problems of repeated searches, static search targets and low efficiency in multi-UAV cooperative search in the prior art, the present invention provides a multi-UAV cooperative search method based on improved pigeon group optimization, which will use The combination of chaos and reverse strategy to initialize the population position, the mutation introduced to avoid falling into local optimum and simulated annealing algorithm can effectively improve the search efficiency of multi-UAVs for dynamic targets and reduce the search repetition area

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