Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem

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

Algorithm 2 Pseudocode of BBFGO

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 the position under binary conditions of each search agent by Equations (11)–(21)
12:
Calculate $f (X_{t + 1})$
13:
end for
14:
Update $X_{G}$ , $X_{P (k)}$ , $f (X_{G})$ and $f (X_{P (k)})$
15:
for each bamboo whip do
16:
Update $C (k)$ using Equation (1)
17:
Update the $X_{t e m p}$ using Equations (7)–(10)
18:
Calculate the position under binary conditions of each search agent by Equations (11)–(21)
19:
Calculate $f (X_{t e m p})$
20:
end for
21:
Update $X_{G}$ , $X_{P (k)}$ , $f (X_{G})$ and $f (X_{P (k)})$
22:
$t = t + 1$
23:
Repeat step 4
24:
end while