title: Is there an accepted "current industry standard best" of stochastic optimization? (Simulated annealing, Particle swarm optimization, etc)
body: <p>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)</p>
  
  <p>Statistical ML algorithms kinda/sorta have winners as well, e.g. "<a href="https://martin-thoma.com/comparing-classifiers/" rel="nofollow noreferrer">You won't go wrong first trying Logistic Regression, Random Forests, and SVM.</a>"</p>
  
  <p><strong>Q: Is there a similar "best of breed" choice between <a href="https://en.wikipedia.org/wiki/Simulated_annealing#Related_methods" rel="nofollow noreferrer">the various global optimum approximation functions</a>?</strong>  For example, it seems that particle swarm optimization (<a href="https://en.wikipedia.org/wiki/Particle_swarm_optimization" rel="nofollow noreferrer">PSO</a>) is several <a href="https://en.wikipedia.org/wiki/Simulated_annealing" rel="nofollow noreferrer">simulated annealing</a> processes running in parallel and sharing information...</p>