An Enhanced Food Digestion Algorithm for Mobile Sensor Localization

. 2023 Aug 29;23(17):7508. doi: 10.3390/s23177508

Algorithm 5 Parallel Compact Food Digestion Algorithm

Output: Population size

N_{p}

; Dimension D; Maximum number of iterations

M a x_{-} i t e r

; Lower boundary

l b

; Upper boundary

u b

;

Input: Global optimal position

B e s t_{-} p

, Global optimal fitness value

B e s t_{-} f

;

1:
Initialize the parameters $a, b, a_{1}, K_{m}, V_{m a x}, i t e r$ and the number of groups $g r o u p s$ as well as the mean and standard deviation $μ$ and $δ$ for each group;
2:
while $i t e r < M a x_{-} i t e r$ do
3:
for $i = 1 : g r o u p s$ do
4:
Sampling generates particles $F o o d_{1}^{t}$ and calculates their fitness values;
5:
Calculate the values of $F 1, F 2, F 3, C 1, C 2, E_{m}$ and S;
6:
Calculate the values of $F 1_{-} d$ and V;
7:
Update the particle to get $F o o d_{2}^{t}$ and calculate its fitness value;
8:
Calculate the values of $F 2_{-} d$ and V;
9:
Update the particle to get $F o o d_{3}^{t}$ and calculate its fitness value;
10:
Calculate the values of $F 3_{-} d$ and V;
11:
Update the particle to get $F o o d_{4}^{t}$ and calculate its fitness value;
12:
Find the particle with the best and worst fitness value among the four particles, denoted as $w i n n e r$ and $l o s e r$ ;
13:
Use $w i n n e r$ and $l o s e r$ to update $P V$ ;
14:
end for
15:
Intergroup communication using parallel strategies;
16:
Find the global optimal solution $B e s t_{-} p$ and its fitness value $B e s t_{-} f$ ;
17:
$i t e r = i t e r + 1$ ;
18:
end while