Random Mutation Hill Climber & Simulated Annealing - Which is Fastest? [closed]

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Closed 9 years ago.

I have used a random mutation hill climbing algorithm as part of a project that I am working on, but was wondering whether it would be better to use simulated annealing to minimise the chance of getting stuck in any local optima.

The question I have is which one tends to be generally faster from your experience? Obviously there is a huge wealth of applications for both algorithms; this is more of a generalised pondering, if you like.

Thank you.

Answer 1

There's no way to tell in advance (unless your project is a 100% match to a well studied academic problem like a pure TSP - and even then ...). It depends on your project's constraints and your project's size (and if you implement the algorithms correctly).

So, to be sure, you have to implement both algorithms (and many others, like Tabu Search, ...) and use a Benchmarker like this one to compare them.

That being said, I 'd put my money on Simulated Annealing over Random Mutation Hill Climbing any day :)

Note: Simulated Annealing is a short but difficult algorithm: I only got it right in my 3th implementation and I 've seen see plenty of wrong implementations (that still output a pretty ok solution) in blogs, etc. It's easier just to reuse optimization algorithms.