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I am curious if any one has comments on "specific characteristics" of a problem that will deem it to be best solved by a specific optimization method such as SimulatedAnnealing, Random Search, or Diferential Evolution, etc.

Edit: I am familiar with the majority of the Mathematica resources which has some useful comments such as:Random search requires that the objective function be locally continuous, DifferentialEvolution is more suitable for discrete optimization problems since it needs a large gene pool, etc. I wanted a discussion expanding that knowledge based on peoples' experience on when they found one method to be more beneficial than other based on the problem characteristics such as size of solution space, non-linearity, processing speed(time), rate of convergence (iterations), etc.

Ps. Thanks for the resources.

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    $\begingroup$ If you haven't seen this page yet, it might be wroth a read. $\endgroup$ – Szabolcs Feb 26 '14 at 23:21
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    $\begingroup$ Also this and this other contain useful information. This one too perhaps. $\endgroup$ – Daniel Lichtblau Feb 26 '14 at 23:55
  • $\begingroup$ @Daniel The 2nd and 3rd link you posted are identical. $\endgroup$ – Szabolcs Feb 27 '14 at 1:10
  • $\begingroup$ This seems like it should be a community-wiki question that ought to have the standard banner warning, "We're looking for long answers that provide some explanation and context..." E.g. mathematica.stackexchange.com/q/18 (Also, the site's format is Q&A, not discussion.) $\endgroup$ – Michael E2 Feb 27 '14 at 11:36
  • $\begingroup$ @Michael, Thanks for your suggestion. I am new to this website and wondering if you could help me doing what you are suggesting. $\endgroup$ – brama Feb 27 '14 at 11:43
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Collecting some links to useful resources from the comments:

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Mathematica Learning Center has a nice tutorial about Constrained Optimization and Unconstrained Optimization available in ebook format (PDF).

You can find more resources in the learning center if you search for "numerical optimization".

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