An enhanced particle swarm optimization based on two-dimensional encoding for moving target search by UAVs

Unmanned aerial vehicle (UAV) has been widely studied because of its high mobility and ability to integrate various functional modules. This article presents an enhanced particle swarm optimization based on two-dimensional encoding (EPSO) to address the moving targets search problem. First, we construct a probability density function to detect the target based on Bayes theory. Next, the EPSO defines UAV search paths through position movement encoding along two coordinate axes and introduces a new moving strategy that balances local and global information. Finally, experimental results demonstrate that EPSO is better than the comparison algorithms, and the average value is increased by 3.8% and the standard deviation is decreased by 53%. These findings confirm that the EPSO improves the stability of the solution while ensuring the quality of the solution.