Algorithm 1. BCDL-GWO algorithm

Input: K population size, maximum iterations T. Output: Optimal solution. Initial: K wolves, t = 0. While t ≤ T  Evaluate fitness value $f(x_l^t)$ for each wolf $x_l^t \forall l=1 to K$.  Find three best leaders, i.e., $x_{\alpha }^t,x_{\beta }^t,x_{\delta }^t$.  Update individual best positions $x_l^b \forall l=1 \mathrm{to} K$.  Update $A_p^t$, and $C_p^t$ using Equations (10) and (11), respectively.  For each wolf in GWO-SCA procedure    Update current position $x_l^{t+1}$ using Equation (19).    Evaluate fitness $f(x_l^{t+1})$.  End For  For each wolf in behavior considerations procedure    Generate new candidate solution $x_l^{BC}$ using Equation (26).    Evaluate fitness $f(x_l^{BC})$.    Update $x_l^{t+1}$ using Equation (27).  End For  For each wolf in dimensional learning procedure    Generate new candidate solution $x_l^{DL}$ using equations (30).    Evaluate fitness $f(x_l^{DL})$.    Update $x_l^{t+1}$ using Equation (31).  End For  Update $x_l^{t+1}$ using Equation (32).  t = t + 1 End While