Sorting algorithms are well understood enough that Java Collections uses some flavor of MergeSort or Timsort. (Even though it is possible to hand-craft collections that "fight" the algorithm and perform poorly, those choices are "often enough ideal" for most real world sorting situations)
Statistical ML algorithms kinda/sorta have winners as well, e.g. "You won't go wrong first trying Logistic Regression, Random Forests, and SVM."
Q: Is there a similar "best of breed" choice between the various global optimum approximation functions? For example, it seems that particle swarm optimization (PSO) is several simulated annealing processes running in parallel and sharing information...