Carrizosa Priego, Emilio JoséDrazic, MilanDrazic, ZoricaMladenović, Nenad2021-04-232021-04-232012-01-01Carrizosa Priego, E.J., Drazic, M., Drazic, Z. y Mladenović, N. (2012). Gaussian variable neighborhood search for continuous optimization. Computers & Operations Research, 39 (9), 2206-2213.0305-05481873-765Xhttps://hdl.handle.net/11441/107639Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained continuous optimization problems. Instead of perturbing the incumbent solution by randomly generating a trial point in a ball of a given metric, we propose to perturb the incumbent solution by adding some noise, following a Gaussian distribution. This way of generating new trial points allows one to give, in a simple and intuitive way, preference to some directions in the search space, or, contrarily, to treat uniformly all directions. Computational results show some advantages of this new approach.application/pdf7 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Global optimizationNonlinear programmingMetaheuristicsVariable neighborhood searchGaussian distributioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1016/j.cor.2011.11.003