Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there a...

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Main Authors: Linlin Li, Shufang Xu, Hua Nie, Yingchi Mao, Shun Yu
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/16/7358
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spelling doaj-573fc7c108bc47868abdb2de6c7ecb742021-08-26T13:29:40ZengMDPI AGApplied Sciences2076-34172021-08-01117358735810.3390/app11167358Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired OptimizationLinlin Li0Shufang Xu1Hua Nie2Yingchi Mao3Shun Yu4School of Computer and Information, Hohai University, Nanjing 211100, ChinaSchool of Computer and Information, Hohai University, Nanjing 211100, ChinaSuma Technology Co., Ltd., Kunshan 215300, ChinaSchool of Computer and Information, Hohai University, Nanjing 211100, ChinaSchool of Computer and Information, Hohai University, Nanjing 211100, ChinaUnmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.https://www.mdpi.com/2076-3417/11/16/7358pigeon-inspired optimizationtent mapdisturbance mechanismcooperative target search
collection DOAJ
language English
format Article
sources DOAJ
author Linlin Li
Shufang Xu
Hua Nie
Yingchi Mao
Shun Yu
spellingShingle Linlin Li
Shufang Xu
Hua Nie
Yingchi Mao
Shun Yu
Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
Applied Sciences
pigeon-inspired optimization
tent map
disturbance mechanism
cooperative target search
author_facet Linlin Li
Shufang Xu
Hua Nie
Yingchi Mao
Shun Yu
author_sort Linlin Li
title Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
title_short Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
title_full Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
title_fullStr Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
title_full_unstemmed Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization
title_sort collaborative target search algorithm for uav based on chaotic disturbance pigeon-inspired optimization
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-08-01
description Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.
topic pigeon-inspired optimization
tent map
disturbance mechanism
cooperative target search
url https://www.mdpi.com/2076-3417/11/16/7358
work_keys_str_mv AT linlinli collaborativetargetsearchalgorithmforuavbasedonchaoticdisturbancepigeoninspiredoptimization
AT shufangxu collaborativetargetsearchalgorithmforuavbasedonchaoticdisturbancepigeoninspiredoptimization
AT huanie collaborativetargetsearchalgorithmforuavbasedonchaoticdisturbancepigeoninspiredoptimization
AT yingchimao collaborativetargetsearchalgorithmforuavbasedonchaoticdisturbancepigeoninspiredoptimization
AT shunyu collaborativetargetsearchalgorithmforuavbasedonchaoticdisturbancepigeoninspiredoptimization
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