An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications

. 2024 Aug 29;9(9):519. doi: 10.3390/biomimetics9090519

Algorithm 1 Framework of the DBO Algorithm

Input:
Maximum iteration $T_{m a x}$ , population size N
Output:
Optimal position $X^{b e s t 2}$ and its corresponding fitness value $f_{\min}$
1:
Initialize the population of particles, indexed as $i = 1, 2 \dots N$ , and def-ine relevant parameters.
2:
while $t \leq T_{\max}$ do
3:
for i belonging to the rolling dung beetles group. do
4:
$a = r a n d (1)$
5:
if $a \leq 0.9$ then
6:
Update the location of the rolling dung beetle using Equation (1).
7:
else
8:
Simulate rolling the ball in the presence of obstacles using Equation (2) to update the location.
9:
end if
10:
end for
11:
Calculate the nonlinear convergence factor as $R = 1 - t / T_{m a x}$ .
12:
for i belonging to the spawning dung beetles group. do
13:
Update the location of the spawning dung beetle using Equations (3) and (4).
14:
end for
15:
for i belonging to the foraging dung beetles group. do
16:
Update the location of the foraging dung beetle using Equations (5) and (6).
17:
end for
18:
for i belonging to the stealing dung beetles group. do
19:
Update the location of the stealing dung beetle using Equation (7).
20:
end for
21:
end while
22:
return Return the optimal position $X^{b e s t 2}$ and its corresponding fitness valu-e $f_{\min}$ .