MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

. 2024 Feb 26;24(5):1502. doi: 10.3390/s24051502

Algorithm 1 Core pseudo codes of the MBB-MOGWO

Input: initial number of the grey wolves n,
the termination criteria

M a x I t

,
the size of archive

A r c S i z e

Output: the current optimal candidate solution set

a r c h i v e

1:
Initialize the grey wolves $X_{i} (i = 1, 2, . . ., n), t, M a x I t$
2:
$a = 2 cos (\frac{t}{M a x I t} \cdot \frac{π}{2})$
3:
$X_{i} . c o s t$ = CalculateAgent( $X_{i} . p o s i t i o n$ )
4:
$a r c h i v e$ = GetNonDominatedParticles(X, $A r c S i z e$ )
5:
$X_{α}$ = ChooseLeader( $a r c h i v e$ ) and Remove $X_{α}$ from the $a r c h i v e$
6:
$X_{β}$ = ChooseLeader( $a r c h i v e$ ) and Remove $X_{β}$ from the $a r c h i v e$
7:
$X_{δ}$ = ChooseLeader( $a r c h i v e$ ) and Re-add $X_{α}$ , $X_{β}$ to the $a r c h i v e$
8:
while ( $t < M a x I t$ ) do
9:
for each $X_{i}$ do
10:
UpdatePosition( $X_{i}$ )
11:
end for
12:
Update a with $a = 2 cos (\frac{t}{M a x I t} \cdot \frac{π}{2})$
13:
Re-calculate $X_{i} . c o s t$ and $a r c h i v e$
14:
if $a r c h i v e$ is full then
15:
Delete solutions in the most crowded grid and add new solution according to S4
16:
else
17:
Update the $a r c h i v e$ according to S1–S3
18:
end if
19:
$X_{α}$ = ChooseLeader( $a r c h i v e$ ) and Remove $X_{α}$ from the $a r c h i v e$
20:
$X_{β}$ = ChooseLeader( $a r c h i v e$ ) and Remove $X_{β}$ from the $a r c h i v e$
21:
$X_{δ}$ = ChooseLeader( $a r c h i v e$ ) and Re-add $X_{α}$ , $X_{β}$ to the $a r c h i v e$
22:
$t = t + 1$
23:
end while
24:
return $a r c h i v e$