Table 5 Pseudocode of the SMA optimization Process.

Input

Model parameters \(\:\theta\:\), dataset \(\:X\)

Output

Optimized model parameters \(\:{\theta\:}^{*}\)

Initialization

Randomly generate initial positions of the slime mould individuals \(\:{X}_{0}\), set the maximum iteration count \(\:T\)

Optimization Process

For each iteration t = 1 to \(\:T\):

1. Compute Fitness

Calculate the fitness value for each individual \(\:{fitness(X}_{t})\)

2. Select Food Sources

Select food sources \(\:{X}_{A}\) and current individual \(\:{X}_{B}\) based on fitness values

3. Update Position

Update the position of individuals \(\:{X}_{t+1}\) based on the distance from food sources and the current individual

4. Update Model Parameters

Update the model parameters \(\:\theta\:\) through the optimization process

Output

Return the optimized model parameters \(\:{\theta\:}^{*}\)