TY - JOUR
T1 - Optimizing the flight route of UAV using biology migration algorithm
AU - Zhang, Q. Y.
AU - He, X. Y.
AU - Dong, Y. Q.
AU - Jiang, S. Y.
AU - Song, H.
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the Jiangsu national science research of high education under Grand 20KJB110021.
PY - 2021/9/28
Y1 - 2021/9/28
N2 - Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. Simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms.
AB - Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. Simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms.
KW - Biology migration algorithm
KW - Trajectory optimization
KW - Unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85117933479&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2025/1/012052
DO - 10.1088/1742-6596/2025/1/012052
M3 - Conference article
AN - SCOPUS:85117933479
VL - 2025
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012052
T2 - 2021 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2021
Y2 - 29 July 2021 through 31 July 2021
ER -