Algorithm 1: SWARAM-based CH selection algorithm

Input: Network population size set to ‘n’ nodes and total count of iterations ‘T’ Output: optimal position of osprey acts as CH node. 1: Initialize network population randomly using Equations (1) and (2). 2: The objective function is computed using Equation (3). 3: For t = 1 to T do. 4: For i = 1 to n do. //exploration phase 5: The fish position is updated for member of OOA using Equation (4). 6: The $SF$ is determined randomly using ith osprey. 7: Osprey’s new position is computed using Equation (8a). 8: The boundary condition is verified using Equation (8b). 9: ith osprey position is updated using Equation (6). //exploitation phase 10: the new position of osprey is computed using Equation (10a). 11: The boundary condition is verified for new position of osprey using Equation (10b). 12: Update the position of osprey using Equation (8). 13: Evaluate the fitness function using Equation (11). 14: If osprey reaches optimal position in network, then 15:  Best candidate osprey act as CH 16: else 17:  Go to step 1. 18: END for 19: END for 20: Return candidate CH.