A simple adaptive algorithm for numerical optimization

Authors:
Francisco Viveros-Jiménez;Jose A. León-Borges;Nareli Cruz-Cortés
Affiliations:
Centro de Investigación en Computación, Instituto Politécnico Nacional, México D.F., México;Universidad Politécnica de Quintana Roo, México;Centro de Investigación en Computación, Instituto Politécnico Nacional, México D.F., México
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Year:
2012

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Abstract

This paper describes a novel algorithm for numerical optimization, which we call Simple Adaptive Climbing (SAC ). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. Our algorithm has a close resemblance to local optimization heuristics such as random walk, gradient descent and, hill-climbing. However, SAC algorithm is capable of performing global optimization efficiently in any kind of space. Tested on 15 well-known unconstrained optimization problems, it confirmed that SAC is competitive against representative state-of-the-art approaches.