Comprehensive Learning Fungal Growth Optimizer for Numerical Optimization and Reservoir Production Optimization

View full-text article in PMC

. 2026 May 27;11(6):370. doi: 10.3390/biomimetics11060370

Algorithm 1 Original Fungal Growth Optimizer (FGO)

1:
Input: Population size N, maximum function evaluations $MaxFEs$ , problem dimension D, bounds $\vec{S_{L}}, \vec{S_{U}}$
2:
Initialize N hyphae $\vec{S_{i}}$ using Equation (1)
3:
Evaluate fitness $f (\vec{S_{i}})$ and identify the global best $\vec{S^{*}}$
4:
$F E s \leftarrow N$ , $t \leftarrow 1$
5:
while $F E s < MaxFEs$ do
6:
Generate random numbers $r_{9}, r_{10} \sim rand (0, 1)$
7:
if $r_{9} < r_{10}$ then ▹ Execute Hyphal Tip Growth loop
8:
for $i = 1$ to N do
9:
if $F E s \geq MaxFEs$ then
10:
break
11:
end if
12:
Compute exploration rate $E_{r}$ and normalized fitness probability $p_{i}$
13:
if $p_{i} < E_{r}$ then
14:
Update $\vec{S_{i}^{t + 1}}$ via Exploration Phase I (Equation (2))
15:
else
16:
Update $\vec{S_{i}^{t + 1}}$ via Exploitation Phase I (Equation (2))
17:
end if
18:
Evaluate $\vec{S_{i}^{t + 1}}$ and set $F E s \leftarrow F E s + 1$
19:
Greedy selection: if $f (\vec{S_{i}^{t + 1}}) < f (\vec{S_{i}^{t}})$ , set $\vec{S_{i}^{t}} \leftarrow \vec{S_{i}^{t + 1}}$
20:
end for
21:
else ▹ Execute Branching and Germination loop
22:
for $i = 1$ to N do
23:
if $F E s \geq MaxFEs$ then
24:
break
25:
end if
26:
Generate random number $r_{7} \sim rand (0, 1)$
27:
if $r_{7} < 0.5$ then
28:
Update $\vec{S_{i}^{t + 1}}$ via Hyphal Branching (Equation (3))
29:
else
30:
Update $\vec{S_{i}^{t + 1}}$ via Spore Germination (Equation (4))
31:
end if
32:
Evaluate $\vec{S_{i}^{t + 1}}$ and set $F E s \leftarrow F E s + 1$
33:
Greedy selection: if $f (\vec{S_{i}^{t + 1}}) < f (\vec{S_{i}^{t}})$ , set $\vec{S_{i}^{t}} \leftarrow \vec{S_{i}^{t + 1}}$
34:
end for
35:
end if
36:
Update global best $\vec{S^{*}}$ if a better solution is found
37:
$t \leftarrow t + 1$
38:
end while
39:
Output: Global best solution $\vec{S^{*}}$