TY - JOUR
T1 - An adaptive single-point algorithm for global numerical optimization
AU - Viveros-Jiménez, Francisco
AU - León-Borges, José A.
AU - Cruz-Cortés, Nareli
N1 - Funding Information:
The first author acknowledges support from CONACYT through a PhD scholarship. The third author acknowledges support from CONACYT through project number 132073.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Adaptive behavior
KW - Hill-climbing
KW - Numerical optimization
KW - Unconstrained problems
UR - http://www.scopus.com/inward/record.url?scp=84887153436&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2013.08.018
DO - 10.1016/j.eswa.2013.08.018
M3 - Artículo
SN - 0957-4174
VL - 41
SP - 877
EP - 885
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -