Algorithm 3. IBPDO

Input: The number of prairie dogs n, the number of prairie dog family coteries m, the attractiveness coefficient ρ of the food-source discovery call, the food abundance parameter ε, and the required number of sensor nodes to be selected Ns.

Output: The globally optimal solution found GBest.

1: Randomly initialize the candidate solutions CT and PD.

2: while $iter<{ Max}_{iter}$ do

3:    for i = 1 to m do

4:      for j = 1 to n do

5:        Evaluate the fitness of each prairie dog individual.

6:        Generate new best and worst candidate solutions using Cauchy mutation and hybrid opposition-based learning, and retain the better of the two.

7:        Update the digging strength DS, the predator influence, and the randomly accumulated ${CPD}_{i,j}$ of PE.

8:        if $iter<{\displaystyle \frac{{Max}_{iter}}{4}}$ then

9:             Simulate the foraging behavior using Equation (15)

10:      else if ${\displaystyle \frac{{Max}_{iter}}{4}} \le iter<{\displaystyle \frac{{Max}_{iter}}{2}}$ then

11:           Simulate the burrowing behavior using Equation (18)

12:      else if ${\displaystyle \frac{{Max}_{iter}}{2}} \le iter<3{\displaystyle \frac{{Max}_{iter}}{4}}$ then

13:           Simulate the convergence toward food sources using Equation (20)

14:      else

15:           Simulate predator-avoidance behavior using Equation (21)

16:      end if

17:       end for

18:     end for

19: Convert the output to binary using the V-shaped transfer function.

20: Adjust the number of selected sensor nodes according to Ns

21: $iter \leftarrow iter+1$

22: end while

23: return GBest