Algorithm 1: pseudo code for LMO in SARP.

Input: Generate a random population of Lions, i.e., sensor nodes $({\mathcal{S}}_{𝒾})$ in a search space $({ℛ}_{𝒾})$ Output: Cluster-based routing solution for BASNs. Procedure: Initialize all sensor nodes with the parameters given in Table 3 1. Begin the iterative methodology and set the round number = ${𝓉}^{+}$ 2. While (number of generations) do 3. Initialize all members of prides and nomad lions on the problem search space. 4. Select a percentage of the nomads ${\mathcal{N}}_{ℒ}$ and the resident lions ${ℛ}_{ℒ}$ such that $\mathcal{N}={ℛ}_{ℒ}+$ ${\mathcal{N}}_{ℒ}$. 5. Both lioness and lion are selected for hunting a prey, randomly. 6. For each lion do 7. Compute the fitness 8. End for 9. If the fitness of a lion is greater than other lion in a pride Then 10.Update the lion in the pride in the problem search space 11.For each lion do 12.Try mating behavior 13.A percentage of nomad females mate with the best males 14.In each pride, a ratio of sex rate ($\mathcal{S}$) is applied to $ℳℒ(𝒾)$ and $ℱℒ(𝒿)$ to produce new cubs 15.If no improvement in fitness value then 16.Try territorial defense behavior 17.If no improvement in fitness value then 18.Try territorial takeover behavior 19.This ratio is inverted in nomad lions. 20.End if 21.End if 22.End while 23.For each pride 24.Both of male and female search randomly in the search space. 25.Nomad males attack prides randomly. 26.End for 27.For each bride 28.Some randomly selected female emigrates from the pride and becomes a nomad. 29.End for 30.Each gender of nomad lions is sorted according to their fitness value. 31.The best females are distributed to the prides to fill the empty places of the immigrated females. 32.Compare and swap the weak lions with best fitness values lions. 33.Memorize the best solution 34.While (next generation until stopping criteria not met) do 35.Send control to line 1 such that $𝓉<𝓉+1$, Else 36.Return the best solution 37.End procedure