Open AccessOptimizing the Flight Route of UAV Using Biology Migration Algorithm
Author(s) -
Q. Y. Zhang,
Xiangyu He,
Yongquan Dong,
Shouyong Jiang,
Huadong Song
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012052
Subject(s) - swarm intelligence , computer science , trajectory , convergence (economics) , swarm behaviour , process (computing) , trajectory optimization , point (geometry) , field (mathematics) , path (computing) , mathematical optimization , real time computing , algorithm , particle swarm optimization , artificial intelligence , mathematics , optimal control , programming language , economics , economic growth , operating system , physics , geometry , astronomy , pure mathematics
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.