Parallelizing a global optimization method in a distributed-memory environment
Parallelizing a global optimization method in a distributed-memory environment
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在分布式内存环境中并行化全局优化方法
DOI：
10.1109/empdp.2000.823390
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
S. Kowalik
中科院分区：
文献类型：
--
作者：
Zdzislaw Szczerbinski;S. Kowalik
我们目前的研究并行化的区域并行方法的全局优化。本文在介绍遗传算法的基础上，提出了遗传算法的并行化模型。本文的第二部分讨论了煤矿地震震源的全局优化问题。首先，简要介绍了震源定位的S-P方法，该方法要求震源定位误差函数最小。其次，给出了一个实际的煤矿开采的例子，地震仪收集的数据和震源的位置是通过采用区域平行的方法。实验结果是从实现的顺序和并行版本的区域并行方法在Sun Ultra工作站的局域网。结果表明，这种优化方法的并行化的岛屿模型的适用性，以及反驳主从模型的有用性。
We present research into parallelizing the zone-parallel method of global optimization. The method belongs to the class of genetic algorithms and is briefly, described in the paper upon introduction to genetic algorithms, parallelization models for genetic algorithms are presented. The subsequent part of the paper is devoted to the global optimization problem of finding sources of tremors in coal mines. First, a short description of the S-P method for localizing hypocenters of tremors is given; the method requires minimizing the error function for hypocenter location. Next, a practical coal-mining example is given where data on a tremor are collected by seismometers and the location of the the hypocenter is found by employing the zone parallel method. Experimental results are presented which were obtained from implementing both the sequential and parallel versions of the zone-parallel method in a local area network of Sun Ultra workstations. The results show suitability of the island model of parallelization for this optimization method as well as disproving the usefulness of the master-slave model.