Central European Journal of Operations Research

, Volume 25, Issue 4, pp 929–952 | Cite as

Empirical study of the improved UNIRANDI local search method

Original Paper


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.


Direct search Local search Global optimization Benchmarking 



This work was supported by the Sapientia Foundation - Institute for Scientific Research with the Grant No. 252/11/28/04/2015.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Faculty of Economics, Socio-Human Sciences and EngineeringSapientia - Hungarian University of TransylvaniaMiercurea CiucRomania

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