Optimization algorithm based on densification and dynamic canonical descent

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:191

Article; Proceedings Paper

Bousson, K ; Correia, SD

关键词: global optimization;    optimal control;    densification curves;    derivative-free methods;    variable reduction;   

DOI  :  10.1016/j.cam.2005.07.023

来源: Elsevier

【 摘 要 】

Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms. (c) 2005 Elsevier B.V. All rights reserved.

【 授权许可】

Free