Paper Info
Title
Empirical study of the improved UNIRANDI local search method.
Abstract
UNIRANDI is a stochastic local search algorithm that performs line searches from starting points along good random directions. In this paper, we focus on a modified version of this method. The new algorithm, addition to the random directions, considers more promising directions in order to speed up the optimization process. 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. It is also compared to the previous version as well as other local search methods. Numerical results show that the new method is promising in terms of robustness and efficiency.
Year
DOI
Venue
2017
https://doi.org/10.1007/s10100-017-0470-2
CEJOR
Keywords
Field
DocType
Direct search,Local search,Global optimization,Benchmarking
Hill climbing,Mathematical optimization,Global optimization,Guided Local Search,CPU time,Beam search,Line search,Local search (optimization),Iterated local search,Mathematics
Journal
Volume
Issue
ISSN
25
4
1435-246X
Citations 
PageRank 
References 
0
0.34
18
Authors
1
Name
Order
Citations
PageRank
László Pál1594.78