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An Adaptive Random Search for Unconstrained Global Optimization
Jonás Velasco, Mario A. Saucedo-Espinosa, Hugo Jair Escalante, Karlo Mendoza, César Emilio Villarreal-Rodríguez, Óscar L. Chacón-Mondragón, Arturo Berrones;
Computación y Sistemas 2014 18(2)
Inglés Español
Adaptive Gibbs Sampling (AGS) algorithm is a new heuristic for unconstrained global optimization. AGS algorithm is a population-based method that uses a random search strategy to generate a set of new potential solutions. Random search combines the one-dimensional Metropolis-Hastings algorithm with the multidimensional Gibbs sampler in such a way that the noise level can be adaptively controlled according to the landscape providing a good balance between exploration and exploitation over all search space. Local search strategies can be coupled to the random search methods in order to intensify in the promising regions. We have performed experiments on three well known test problems in a range of dimensions with a resulting testbed of 33 instances. We compare the AGS algorithm against two deterministic methods and three stochastic methods. Results show that the AGS al- gorithm is robust in problems that involve central aspects which is the main reason of global optimization problem difficulty including high-dimensionality, multi-modality and non-smoothness.

Palabras clave: Random search, Metropolis-Hastings algo- rithm, heuristics, global optimization.
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Universidad Autónoma del Estado de México
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Versión 3.0 | 2017