Lloyd's algorithm is the standard k-means clustering algorithm and it is iterative. You chose
k random starting points from your data set and compute the (Euclidean) distance of each point to each cluster center. You assign each point to its nearest cluster. After the assignment of one point to one cluster, you recalulate the mean of that cluster. After you've done this for all points, your first iteration is complete and you start from the beginning, i.e. you start a new iteration. The cluster means will have changed during your first iteration, which makes it necessary to, again, calculate the distance of each point to each cluster center, and see if there aren't perhaps some points that are now closer to other cluster centres.
In principle you could also stop the Lloyd's algorithm after your first round of assigning points and your algorithm would be non-iterative. The quality of your solution will, however, be not that great.
MacQueen's algorithm is non-iterative in the sense you choose your
k random starting points and assign all your points to those centers only once. Algorithmically, MacQueen's does nearly the the same thing as Lloyd's: you go through each point and compute the increase in variance for each cluster (this is where it is mathematically different to Lloyd's), if it were assigned to it. You chose to assign the point to the cluster, whose variance increase is minimally. After you have gone through all points once you stop and your MacQueen's algorithm terminates. From an algorithmic point of view, this is where MacQueen's and Lloyd's are different.
In principle you could also continue with more iterations, this would make your algorithm iterative.