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An adaptive single-point algorithm for global numerical optimization.
- Source :
-
Expert Systems with Applications . Feb2014, Vol. 41 Issue 3, p877-885. 9p. - Publication Year :
- 2014
-
Abstract
- Abstract: This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 41
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 91600487
- Full Text :
- https://doi.org/10.1016/j.eswa.2013.08.018