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Empirical study of the improved UNIRANDI local search method. (English) Zbl 1390.90591
Summary: 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.

MSC:
90C59 Approximation methods and heuristics in mathematical programming
90C26 Nonconvex programming, global optimization
90C60 Abstract computational complexity for mathematical programming problems
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