Multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization

Abstract

The invention discloses a multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization. The method comprises the following steps: firstly establishing a mapmodel, and establishing a target information map by using a Markov model, and then establishing a motion model and a digital pheromone map of the unmanned aerial vehicle; performing the collaborativesearching of multi-unmanned aerial vehicle by applying the pigeon flock optimization algorithm, and realizing by using chaotic and reverse strategies at an initial population location, thereby determining the randomness of the initial location; when performing the iteration of the map and the compass operator at the first stage, preventing from falling in local optimum by using Cauchy mutation; when performing the iteration of a landmark operator, representing the bad individual and Gaussian variation by using simulated annealing reservation part, thereby preventing premature from falling inlocal optimum. The chaotic and reverse strategies are used for initializing the population location, the variation and simulated annealing algorithm imported for preventing local optimum are combined,thereby solving the dynamic target and repeated searching problem in the searching process, and the searching efficiency is effectively improved at the same time.

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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