CSM-416 A survey of AI-based meta-heuristics for dealing with local optima in local search
Patrick Mills, Edward P. K. Tsang, Qingfu Zhang, John Alexander Ford · Open Access at Essex (University of Essex) · 2004
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.