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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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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