A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning
Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenar...
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doaj-8857d9b20f67442391dff84f7cbdeffe2021-04-26T23:03:52ZengMDPI AGSensors1424-82202021-04-01213037303710.3390/s21093037A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path PlanningLisu Huo0Jianghan Zhu1Zhimeng Li2Manhao Ma3College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaUnmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.https://www.mdpi.com/1424-8220/21/9/3037unmanned aerial vehiclepath planningdifferential evolutionsymbiotic organism searchparticle swarm optimizationevolutionary algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lisu Huo Jianghan Zhu Zhimeng Li Manhao Ma |
spellingShingle |
Lisu Huo Jianghan Zhu Zhimeng Li Manhao Ma A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning Sensors unmanned aerial vehicle path planning differential evolution symbiotic organism search particle swarm optimization evolutionary algorithm |
author_facet |
Lisu Huo Jianghan Zhu Zhimeng Li Manhao Ma |
author_sort |
Lisu Huo |
title |
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning |
title_short |
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning |
title_full |
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning |
title_fullStr |
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning |
title_full_unstemmed |
A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning |
title_sort |
hybrid differential symbiotic organisms search algorithm for uav path planning |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-04-01 |
description |
Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm. |
topic |
unmanned aerial vehicle path planning differential evolution symbiotic organism search particle swarm optimization evolutionary algorithm |
url |
https://www.mdpi.com/1424-8220/21/9/3037 |
work_keys_str_mv |
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1721507242035904512 |