A survey of AI-based meta-heuristics for dealing with local optima in local search
نویسندگان
چکیده
Meta-heuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast meta-heuristics for local search. First, we present the technique of local search (or hill climbing as it is sometimes known). We then present a table displaying the attributes of all the different meta-heuristics. After this, we give a short description and discussion of each meta-heuristic with pseudo code. Finally, we describe why, in general, these techniques work and present some ideas of what is needed from the next generation of meta-heuristics.
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