Artículo
Gaussian variable neighborhood search for continuous optimization
Autor/es | Carrizosa Priego, Emilio José
Drazic, Milan Drazic, Zorica Mladenović, Nenad |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2012-01-01 |
Fecha de depósito | 2021-04-23 |
Publicado en |
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Resumen | 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 ... 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. |
Cita | Carrizosa 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. |
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