Authors: Carrizosa, Emilio
Dražić, Milan
Dražić, Zorica
Mladenović, Nenad 
Title: Gaussian variable neighborhood search for continuous optimization
Journal: Computers and Operations Research
Volume: 39
Issue: 9
First page: 2206
Last page: 2213
Issue Date: 1-Sep-2012
Rank: M21
ISSN: 0305-0548
DOI: 10.1016/j.cor.2011.11.003
Abstract: 
Variable 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.
Keywords: Gaussian distribution | Global optimization | Metaheuristics | Nonlinear programming | Variable neighborhood search
Publisher: Elsevier
Project: Ministerio de Educación y Ciencia, Spain, Grant nos. MTM2009-14039 and SAB2009-0144
Junta de Andalucía, Spain, Grant no. FQM329
Mathematical Modelas and Optimization Methods on Large-Scale Systems 

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