Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
474705 | Computers & Operations Research | 2012 | 8 Pages |
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.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Emilio Carrizosa, Milan Dražić, Zorica Dražić, Nenad Mladenović,