Continuous GRASP with a local active-set method for bound-constrained global optimization

被引：0
|
作者：
Ernesto G. Birgin
Erico M. Gozzi
Mauricio G. C. Resende
Ricardo M. A. Silva
机构：
[1] Universidade de São Paulo,Instituto de Matemática e Estatística
[2] AT&T Labs Research,Algorithms and Optimization Research Department
[3] Federal University of Lavras,Department of Computer Science
来源：
Journal of Global Optimization | 2010年 / 48卷
关键词：
Global optimization; Stochastic methods; Active-set methods; Heuristic; CGRASP; GENCAN;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic—based on the CGRASP and GENCAN methods—for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP–GENCAN on a set of benchmark multimodal test functions.
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收藏
页码：289 / 310
页数：21
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