Heterogeneous computing and parallel genetic algorithms

被引:52
作者
Alba, E [1 ]
Nebro, AJ [1 ]
Troya, JM [1 ]
机构
[1] Univ Malaga, Dept Lenguajes & Ciencias Computac, ETSI Informat, E-29071 Malaga, Spain
关键词
parallel genetic algorithms; !text type='Java']Java[!/text; heterogeneous computational systems; speedup;
D O I
10.1006/jpdc.2002.1851
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution. we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines linked by different kinds of communication networks. This algorithm benefits from the computational resources offered by modern LANs and by Internet, therefore allowing researchers to solve more difficult problems by using a large set of available machines. We analyze the way in which such heterogeneous systems affect the genetic search for two problems. Our conclusion is that super-linear performance can be achieved not only in homogeneous but also in heterogeneous clusters of machines. In addition, we study some special features of the running platforms for PGAs, and basically find out that heterogeneous computing can be as efficient or even more efficient than homogeneous computing for parallel heuristics. (C) 2002 Elsevier Science (USA)
引用
收藏
页码:1362 / 1385
页数:24
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