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

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

Algorithm 2 Framework of the ADBO 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 define relevant parameters, setting $ω_{\max}$ A and $ω_{\min}$ .
2:
while $t \leq T_{\max}$ do
3:
Initialize dung beetle positions using Gaussian chaotic mapping according to Equation (8).
4:
Update the weight factors using Equation (13).
5:
for i belonging to the rolling dung beetles group. do
6:
$a = r a n d (1)$
7:
if $a \leq 0.9$ then
8:
Update the location of the rolling dung beetle using Equation (1).
9:
else
10:
Simulate rolling the ball in the presence of obstacles using Equation (2) to update the location.
11:
end if
12:
end for
13:
Calculate the nonlinear convergence factor as $R = 1 - t / T_{m a x}$ .
14:
for i belonging to the spawning dung beetles group. do
15:
Using Equation (3) to determine the range of spawning dung beetles and Equation (11) to update the position of the spawning dung beetles.
16:
end for
17:
for i belonging to the foraging dung beetles group. do
18:
Determine the range of foraging dung beetles using Equation (5) and update the position of the foraging dung beetles using Equation (12).
19:
end for
20:
for i belonging to the stealing dung beetles group. do
21:
Update the location of the stealing dung beetle using Equation (13).
22:
end for
23:
end while
24:
return Return the optimal position $X^{b e s t 2}$ and its corresponding fitness value $f_{\min}$ .