Algorithm 1. Standard flower pollination algorithm.

1: Define the objective function $f(x),x=(x_1,x_2,\dots ,x_d)$ 2: Initialize a population of n flowers/pollen gametes with random solutions 3: Evaluate each flower or solution in the population 4: Extract the best solution in the population 5: Find the best solution $g_{best}^{*}$ in the initial population 6: Define a switch probability $p\in [0,1]$ 7: Define fixed number of iterations $Max\_generation$ 8: While $t<Max\_generation$ 9: for i = 1:n (each flower in the population) 10: if (rand < p) 11: Draw a (d-dimensional) step vector L which obeys a Lévy distribution 12: Global pollination via $x_i^{t+1}=x_i^t+\gamma L\left(\lambda \right)(x_i^t-g_{best}^{*})$ 13: else 14: Draw $\epsilon$ from a uniform distribution in [0, 1] 15: Do local pollination via $x_i^{t+1}=x_i^t+\epsilon (x_j^t-x_k^t)$ 16: end if 17: Evaluate each new solution $x_i^t$ 18: If new solution is better, update it in the population 19: end for 20: Find the current best solution $g_{best}^{*}$ 21: end While 22: Output the best solution found