Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem

. 2023 Feb 8;25(2):314. doi: 10.3390/e25020314

Algorithm 1 Pseudocode of BFGO

1:
Initialize the parameters of BFGO: N (number of search agents), T (maximum iteration), n (number of bamboo shoots) and K (number of bamboo whips)
2:
Initialize the positions of the search agents
3:
Calculate the fitness of each search agent
4:
The search agents are sorted in descending order of fitness and divided into K groups according to the uniform grouping in Figure 2
5:
Update $X_{G}$ , $X_{P (k)}$ , $f (X_{G})$ and $f (X_{P (k)})$
6:
$t = 2$
7:
while $t < T + 1$ do
8:
for each bamboo whip do
9:
Update $C (k)$ using Equation (1)
10:
Update the $X_{t + 1}$ using Equations (2)–(6)
11:
Calculate $f (X_{t + 1})$
12:
end for
13:
Update $X_{G}$ , $X_{P (k)}$ , $f (X_{G})$ and $f (X_{P (k)})$
14:
for each bamboo whip do
15:
Update $C (k)$ using Equation (1)
16:
Update the $X_{t e m p}$ using Equations (7)–(10)
17:
Calculate $f (X_{t e m p})$
18:
end for
19:
Update $X_{G}$ , $X_{P (k)}$ , $f (X_{G})$ and $f (X_{P (k)})$
20:
$t = t + 1$
21:
Repeat step 4
22:
end while