A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial–Temporal Distribution of Users and Advanced Genetic Algorithms

. 2022 Oct 18;22(20):7930. doi: 10.3390/s22207930

Algorithm 2: Crossover

1.
Input: $P o p u l a t i o n [N, k]$ $; f i t n e s s_{a v e}$ $; f i t n e s s (i)$ $; A_{i j} (T_{i - δ}), P (A_{i j} (T_{i - δ})), δ = 1 \dots h;$
2.
Output: $P o p u l a t i o n [e n d, k]$ ;
3.
$i = 1$
4.
while $i \leq$ N do
5.
if $f i t n e s s (i) \geq f i t n e s s_{a v e}$ then
6.
if $rand \leq P_{a c r} (0) \times \frac{f i t n e s s_{m a x} - f i t n e s s}{f i t n e s s_{m a x} - f i t n e s s_{a v e}}$ then
7.
Randomly choose a crossover starting node
8.
Randomly choose the crossover length
9.
$P o p u l a t i o n [i, k]$ perform a crossover operation with $P o p u l a t i o n [i + 1, k]$
10.
Sub-individuals are inserted at the end of the population
11.
$i = i + 2$
12.
else
13.
$i = i + 1$
14.
end if
15.
else
16.
if $rand \leq P_{a c r} (1)$ then
17.
Select prior high fitness crossover objects by selection probability
18.
Randomly choose a crossover starting node
19.
Randomly choose the crossover length
20.
$P o p u l a t i o n [i, k]$ perform a crossover operation with $A_{i j} (T_{i - δ})$
21.
Sub-individuals are inserted at the end of the population
22.
$i = i + 1$
23.
else
24.
$i = i + 1$
25.
end if
26.
end if
27.
end while