UAV route planning is one of the key issues for autonomous flight of UAVs. Particle Swarm Optimization (PSO), as an optimization algorithm, is widely used in UAV route planning. This article will introduce the application of MATLAB-based particle swarm algorithm in UAV route planning and provide the corresponding source code.
Particle swarm optimization is an optimization algorithm that simulates the foraging behavior of a flock of birds. It finds the optimal solution by simulating the search process of particles in the solution space. In UAV route planning, we can regard each particle as a possible route solution, and the position of the particle represents the flight path of the UAV. The particle swarm algorithm gradually finds the optimal route planning solution by iteratively updating the position and speed of particles.
The following is the source code for the implementation of MATLAB-based particle swarm algorithm in UAV route planning:
% 初始化参数
numParticles = 50; % 粒子数量
maxIterations = 100; % 最大迭代次数
c1 = 2; % 加速度常数
c2