A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning
This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired optimization (PIO) is proposed, which incorporates a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/910 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589192107917312 |
|---|---|
| author | Yankai Shen Xinan Liu Xiao Ma Hong Du Long Xin |
| author_facet | Yankai Shen Xinan Liu Xiao Ma Hong Du Long Xin |
| author_sort | Yankai Shen |
| collection | DOAJ |
| description | This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired optimization (PIO) is proposed, which incorporates a bionic social learning strategy. In this modification, the global best is replaced by the average of the top-ranked solutions in the map and compass operator, while the global center is replaced by the local center in the landmark operator. The paper also proves the algorithm’s convergence and provides complexity analysis. Comparison experiments demonstrate that the proposed method searches for the optimal solution while guaranteeing fast convergence. Subsequently, a path-planning model, detection units’ network model, and cost estimation are constructed. The developed BSLSPIO is utilized to generate feasible paths for UAVs, adhering to time consistency constraints. The simulation results show that the BSLSPIO generates feasible paths at minimum cost and effectively solves the UAVs’ cooperative path-planning problem. |
| format | Article |
| id | doaj-art-769bf58e71c945ccabe917636672532a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-769bf58e71c945ccabe917636672532a2025-01-24T13:21:18ZengMDPI AGApplied Sciences2076-34172025-01-0115291010.3390/app15020910A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path PlanningYankai Shen0Xinan Liu1Xiao Ma2Hong Du3Long Xin4China North Vehicle Research Institute, Beijing 100072, ChinaChina North Vehicle Research Institute, Beijing 100072, ChinaChina North Vehicle Research Institute, Beijing 100072, ChinaChina North Vehicle Research Institute, Beijing 100072, ChinaBeijing Institute of Astronautical Systems Engineering, Beijing 100076, ChinaThis paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired optimization (PIO) is proposed, which incorporates a bionic social learning strategy. In this modification, the global best is replaced by the average of the top-ranked solutions in the map and compass operator, while the global center is replaced by the local center in the landmark operator. The paper also proves the algorithm’s convergence and provides complexity analysis. Comparison experiments demonstrate that the proposed method searches for the optimal solution while guaranteeing fast convergence. Subsequently, a path-planning model, detection units’ network model, and cost estimation are constructed. The developed BSLSPIO is utilized to generate feasible paths for UAVs, adhering to time consistency constraints. The simulation results show that the BSLSPIO generates feasible paths at minimum cost and effectively solves the UAVs’ cooperative path-planning problem.https://www.mdpi.com/2076-3417/15/2/910pigeon-inspired optimization (PIO)bionic social learning strategy (BSLS)multi-UAV cooperative path planning |
| spellingShingle | Yankai Shen Xinan Liu Xiao Ma Hong Du Long Xin A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning Applied Sciences pigeon-inspired optimization (PIO) bionic social learning strategy (BSLS) multi-UAV cooperative path planning |
| title | A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning |
| title_full | A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning |
| title_fullStr | A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning |
| title_full_unstemmed | A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning |
| title_short | A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning |
| title_sort | bionic social learning strategy pigeon inspired optimization for multi unmanned aerial vehicle cooperative path planning |
| topic | pigeon-inspired optimization (PIO) bionic social learning strategy (BSLS) multi-UAV cooperative path planning |
| url | https://www.mdpi.com/2076-3417/15/2/910 |
| work_keys_str_mv | AT yankaishen abionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT xinanliu abionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT xiaoma abionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT hongdu abionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT longxin abionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT yankaishen bionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT xinanliu bionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT xiaoma bionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT hongdu bionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning AT longxin bionicsociallearningstrategypigeoninspiredoptimizationformultiunmannedaerialvehiclecooperativepathplanning |