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Communication Dans Un Congrès Année : 2022

Escaping Local Optima With Local Search: A Theory-Driven Discussion

Résumé

Local search is the most basic strategy in optimization settings when no specific problem knowledge is employed. While this strategy finds good solutions for certain optimization problems, it generally suffers from getting stuck in local optima. This stagnation can be avoided if local search is modified. Depending on the optimization landscape, different modifications vary in their success. We discuss several features of optimization landscapes and give analyses as examples for how they affect the performance of modifications of local search. We consider modifying random local search by restarting it and by considering larger search radii. The landscape features we analyze include the number of local optima, the distance between different optima, as well as the local landscape around a local optimum. For each feature, we show which modifications of local search handle them well and which do not.
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Dates et versions

hal-03703829 , version 1 (24-06-2022)

Identifiants

Citer

Tobias Friedrich, Timo Kötzing, Martin S Krejca, Amirhossein Rajabi. Escaping Local Optima With Local Search: A Theory-Driven Discussion. Parallel Problem Solving from Nature (PPSN) XVII, Sep 2022, Dortmund, Germany. pp.442-455, ⟨10.1007/978-3-031-14721-0_31⟩. ⟨hal-03703829⟩
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