An Adaptive Random Search for Unconstrained Global Optimization

Authors

  • Jonas Velasco Universidad Autónoma de Nuevo León.
  • Mario A. Saucedo-Espinosa Universidad Autónoma de Nuevo León.
  • Hugo Jair Escalante Instituto Nacional de Astrofísica, Óptica y Electrónica,
  • Karlo Mendoza Universidad Autónoma de Nuevo León.
  • César E. Villarreal-Rodríguez Universidad Autónoma de Nuevo León.
  • Óscar L. Chacón-Mondragón Universidad Autónoma de Nuevo León.
  • Arturo Berrones Universidad Autónoma de Nuevo León.

DOI:

https://doi.org/10.13053/cys-18-2-1419

Keywords:

Random search, Metropolis-Hastings algorithm, heuristics, global optimization

Abstract

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 algorithm 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.

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Published

2014-06-30

Issue

Vol. 18 No. 2 (2014): 18(2) April-June 2014

Section

Articles

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