Algorithm 1 Pseudo-code of the GOA.

Input:  $\mathrm{N}$: population size; $\mathrm{Dim}$: problem dimension; $\mathrm{Tmax}\_\mathrm{iter}$: maximum number of iterations; Output:  The position of the best individual and its fitness value;   1:Initialize the population $\mathrm{X}$ randomly, $\mathrm{r}$ and $\mathrm{q}$ are all random numbers from 0 to 1;   2:Generate memory matrix MX;   3:Calculate the fitness value of X;   4:while stopping condition is not met do   5:      if $\mathrm{r}>0.5$ then   6:         for ${\mathrm{MX}}_{\mathrm{i}}$ do   7:             if $\mathrm{q}\ge 0.5$ then   8:                   Update the location Gannet using Equation (7),where $\mathrm{q}\ge 0.5$;   9:             else 10:                   Update the location Gannet using Equation (7),where $\mathrm{q}<0.5$; 11:             end if 12:         end for 13:    else 14:         for ${\mathrm{MX}}_{\mathrm{i}}$ do 15:             if $\mathrm{C}\ge 0.2$ then 16:                   Update the location Gannet using Equation (16),where $\mathrm{C}\ge 0.2$; 17:             else 18:                   Update the location Gannet using Equation (16),where $\mathrm{C}<0.2$; 19:             end if 20:         end for 21:    end if 22:    for ${\mathrm{MX}}_{\mathrm{i}}$ do 23:          Calculate the fitness value of $M{X}_i$; 24:          If the value of ${\mathrm{MX}}_{\mathrm{i}}$ is better than the value of ${\mathrm{X}}_{\mathrm{i}}$, replace ${\mathrm{X}}_{\mathrm{i}}$ with ${\mathrm{MX}}_{\mathrm{i}}$; 25:    end for 26:end while