Algorithm 1 Pseudocode for the MSCSO optimization algorithm

1: Initialization parameters: population size $N$, maximum iteration count $T_{\max }$, upper and lower limits of solutions $ub$ and $lb$. 2: Initialize the optimal position of the sand cat $P_b$ and the optimal fitness value $P_{best}$. 3: Fitness function $f(x)$: see Equation (19). 4: Population initialization: to initialize the sand cat population, use Equations (1) and (2) to compute fitness value $f(P_i)$ of every sand cat individual, and take the first $N$ to form a new sand cat population. 5: While $t<T_{\max }$ do 6: for every individual sand cat $P_i$ do 7:  To ensure that each individual sand cat $P_i$ is in the limits of the solution and to calculate the $P_i$, the fitness value of the $f(P_i)$ is used. 8:   if $f(P_i)<P_{best}$, then 9:    Assign the value of $f(P_i)$ to $P_{best}$, updating the optimal location $P_b$. 10:  end if 11: end for 12: Set S to 2 and initialize rg and r according to Equations (3) and (4). 13:  if $\left|R\right|$ > 1 then 14:  Update individual positions according to Equation (5). 15:  Use the Weibull flight strategy to obtain a new position, see Equation (6). 16: else 17:  Adjust positions based on Formula (9). 18:  Use the triangle parade to obtain a new position, see Equation (10). 19: end if 20: Calculate the fitness of the individual value $f(P_i)$ and the new position of fitness value $f(P_{newi})$. 21: if $f(P_{newi})<f(P_i)$, then 22:   Assign $f(P_{newi})$ to $P_i$. 23: end if 24: Use Formulas (15) and (18) to update the location of the entire sand cat population. 25: Use Formula (19) to ensure excellence. 26: If the number of iterations increases by 1, t = t + 1. 27: end while