Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network

. 2024 Mar 7;24(6):1742. doi: 10.3390/s24061742

Algorithm 2 eGA-based UAV trajectory optimization algorithm

1:
Initialize flight time $t_{F}$ with $L$ , eGA-related parameters $N_{p}$ , $N_{g}$ , $P_{c}$ , $P_{m}$ , $P_{o p t}$ .
2:
Generate the initial population with $N_{p}$ chromosomes.
3:
Set the index of generation $n = 0$ .
4:
repeat
5:
Update the fitness values of $N_{p}$ chromosomes according to (21) and select the best chromosome remaining.
6:
Select the parent chromosomes with the binary tournament operator for mating.
7:
Perform the ordered crossover operations with probability $P_{c}$ and shuffle indexes mutation operations with probability $P_{m}$ on parent chromosomes.
8:
Perform the 2-OPT algorithm for local optimization with probability $P_{o p t}$ .
9:
Update the fitness values of new chromosomes and generate the new population with $N_{p}$ chromosomes according to the elitist-preserving strategy.
10:
Update $n \leftarrow n + 1$ .
11:
until $n \geq N_{g}$ .
12:
Select the best chromosome in the final generation as the best visiting sequence, i.e., $q = a r g m a x f (q)$ .