Abstract
Local search is a crucial part of the GLOBAL algorithm; hence the performance of it depends much on the attached local search method. During the past research, many local search methods were tested as part of GLOBAL. In this study, an enhanced variant of the UNIRANDI local search algorithm is presented. The performance of the new method is tested empirically on standard test functions in terms of function evaluations, success rates, error values, and CPU time. The new UNIRANDI is compared to well-known derivative-free local search methods including state-of-the-art methods too. A performance profile is also used to assess the examined methods.
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Bánhelyi, B., Csendes, T., Lévai, B., Pál, L., Zombori, D. (2018). Local Search. In: The GLOBAL Optimization Algorithm . SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-02375-1_2
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