Asynchronous parallel pattern search for nonlinear optimization
Asynchronous parallel pattern search for nonlinear optimization
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DOI：
10.1137/s1064827599365823
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发表时间：
2001-06-27
影响因子：
3.1
通讯作者：
Torczon, VJ
中科院分区：
文献类型：
--
作者：
Hough, PD;Kolda, TG;Torczon, VJ
提出了一种新的异步并行模式搜索算法APPS。并行模式搜索对于工程优化问题非常有用，这些问题的特点是变量数量很少（比如50个或更少），目标函数的计算成本很高，比如那些由复杂模拟定义的问题，这些问题可能需要几秒钟到几个小时的时间来运行。APPS的目标平台是现在广泛使用的松耦合并行系统。我们利用模式搜索的算法特性来设计变体，这些变体仅在响应消息时动态启动操作，而不是常规地循环执行一组固定的步骤。这提供了一个通用的并发策略，使我们能够有效地平衡所有可用处理器的计算负载。此外，它使我们能够在几乎没有额外开销的情况下实现高度的容错。我们证明了APPS的初步实施标准的测试问题，以及一些工程优化问题的有效性。
We introduce a new asynchronous parallel pattern search ( APPS). Parallel pattern search can be quite useful for engineering optimization problems characterized by a small number of variables ( say fifty or less) and by objective functions that are expensive to evaluate, such as those defined by complex simulations that can take anywhere from a few seconds to many hours to run. The target platforms for APPS are the loosely coupled parallel systems now widely available. We exploit the algorithmic characteristics of pattern search to design variants that dynamically initiate actions solely in response to messages, rather than routinely cycling through a fixed set of steps. This gives a versatile concurrent strategy that allows us to effectively balance the computational load across all available processors. Further, it allows us to incorporate a high degree of fault tolerance with almost no additional overhead. We demonstrate the effectiveness of a preliminary implementation of APPS on both standard test problems as well as some engineering optimization problems.