An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

. 2022 Nov 21;12(11):2892. doi: 10.3390/diagnostics12112892

Algorithm 1 AL-Biruni Earth radius (BER) algorithm

1:
Initialize BER population $S_{i} (i = 1, 2, \dots, d)$ with size d, iterations $T_{m a x}$ , fitness function $F_{n}$ , $t = 1$ , BER parameters
2:
Calculate fitness function $F_{n}$ for each $S_{i}$
3:
Find best solution as $S^{*}$
4:
while $t \leq T_{m a x}$ do
5:
for ( $i = 1 : i < n_{1} + 1$ ) do
6:
Update $r_{1} = h_{1} \frac{c o s (x)}{1 - c o s (x)}$ , $r_{2} = h_{2} \frac{c o s (x)}{1 - c o s (x)}$
7:
Update positions to move toward best solution as in Equation (1)
8:
end for
9:
for ( $i = 1 : i < n_{2} + 1$ ) do
10:
Update $r = h \frac{c o s (x)}{1 - c o s (x)}$ , $r_{3} = h_{3} \frac{c o s (x)}{1 - c o s (x)}$
11:
Update positions Elitism of best solution as in Equation (2)
12:
Update positions Investigating area around best solution as in Equation (3)
13:
Compare $S (t + 1)$ and $S^{'} (t + 1)$ to select best solution $S^{*}$
14:
if The best fitness has remained the same over the past two editions. then
15:
Mutate solution as in Equation (4)
16:
end if
17:
end for
18:
Update the fitness function $F_{n}$ for each $S_{i}$
19:
Find best solution as $S^{*}$
20:
Update BER parameters, $t = t + 1$
21:
end while
22:
Return $S^{*}$