Multialgorithms for parallel computing: A new paradigm for optimization

被引：0
|
作者：
Nazareth, JL [1 ]
机构：
[1] Washington State Univ, Dept Pure & Appl Math, Pullman, WA 99164 USA
来源：
STOCHASTIC OPTIMIZATION: ALGORITHMS AND APPLICATIONS | 2001年 / 54卷
关键词：
optimization; multialgorithms; conjugate gradients; evolutionary algorithms; population-based methods; genetic algorithms; variation; parallel computing;
D O I：
暂无
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
Fixed-population and evolving-population multialgorithms are introduced. They constitute a new population-based paradigm for optimization at the confluence of traditional optimization techniques, evolutionary algorithms and parallel computing. A detailed illustration is given within the context of a new two-parameter family of nonlinear conjugate gradient algorithms and a set of four standard test problems. The illustration provides a platform for a discussion of several major themes that arise in the study of multialgorithms in general.
引用
收藏
页码：183 / 222
页数：40
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