Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm

. 2020 Jul 22;20(15):4065. doi: 10.3390/s20154065

Algorithm 1 Grey wolf optimization

1:
Initialize the number of iterations for optimization $N_{i t e r}$
2:
Initialize the positions of n grey wolves $X_{i}$ , $i =$ 1, 2, …, N
3:
Calculate the fitness value of each grey wolf
4:
Choose the best three grey wolves as $X_{α}$ , $X_{β}$ , $X_{δ}$ base on there fitness
5:
$t \leftarrow 0$ .
6:
whilet < $N_{i t e r}$ do
7:
Update the position of the wolves using to Equation (6)
8:
Update $α$ , A, and C
9:
Calculate the fitness of each grey wolf
10:
Update the first three grey wolves $X_{α}$ , $X_{β}$ , $X_{δ}$
11:
$t \leftarrow t + 1$ .
12:
end while
13:
return $b e s t f i t n e s s, X_{α}$