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首页> 外文期刊>Future generation computer systems >Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization
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Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization

机译：网格使能，分层分布式元模型辅助的进化算法，用于空气动力学形状优化

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摘要

A Grid-enabled optimization environment is presented. It is based on Metamodel-Assisted Evolutionary Algorithms (MAEAs), where radial basis function networks, trained on the fly on selected subsets of the previously evaluated individuals, are used to pre-evaluate the population members. The search follows a Hierarchical and Distributed scheme (HDMAEA), with more than one search level, each of which is associated with a different problem-specific evaluation tool and a different number of semi-isolated demes. Irrespective of the use of cluster or Grid computing, the HDMAEA drastically reduces the number of evaluations required to reach the optimal solution(s). The Grid-enabled HDMAEA, based on the master-slave model with simultaneously evaluated population members, aims at solving large scale optimization problems in affordable wall clock time. In the proposed Grid-computing setup, Condor is used as the local resource manager on each contributing cluster, authentication and interfacing is carried out via the Globus Toolkit and the unification of Grid resources under a common queue is undertaken by the Gridway metascheduler. If more than one search level are used (hierarchical search), the optimization of Grid resources' allocation relies on the distinction between computationally demanding, high-accuracy and less demanding, low-accuracy evaluation tools. The proposed Grid-enabled problem solving environment is demonstrated on three aerodynamic shape optimization problems, namely the design of a compressor cascade and two 3D elbow ducts, on three remote clusters.
机译：提出了支持网格的优化环境。它基于元模型辅助进化算法（MAEA），其中径向基函数网络是在先前评估的个体的选定子集上进行动态训练的，用于对种群成员进行预评估。搜索遵循分层和分布式方案（HDMAEA），具有一个以上的搜索级别，每个搜索级别都与不同的特定于问题的评估工具和不同数量的半隔离主题相关联。无论使用群集计算还是网格计算，HDMAEA都可以大大减少达到最佳解决方案所需的评估数量。基于网格的HDMAEA基于主从模型并同时评估了人口成员，旨在解决可负担的挂钟时间中的大规模优化问题。在建议的网格计算设置中，将Condor用作每个贡献群集上的本地资源管理器，通过Globus Toolkit进行身份验证和接口连接，并由Gridway元调度程序在公共队列下统一Grid资源。如果使用一个以上的搜索级别（分层搜索），则网格资源分配的优化取决于计算要求高，准确性高，要求低，准确性低的评估工具之间的区别。提出的具有网格功能的问题解决环境在三个空气动力学形状优化问题上得到了证明，这三个问题是在三个远程集群上的一个压缩机级联和两个3D弯管的设计。

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