全局函数优化中的混合遗传算法和模拟退火算法(HGASA)

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI:10.1109/ICTAI.2005.72

Dingjun Chen, Chung-Yeol Lee, C. Park

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引用次数: 19

摘要

我们已经在一台Sun工作站机器上实现了顺序hgaa;与一些效率低、可靠性有限的顺序优化算法相比，它在寻找样本函数优化问题的全局最优方面表现得非常好。然而，顺序hgaa通常需要较长的运行时间成本。因此，我们在高性能计算机上使用消息传递接口(MPI)实现了并行HGASA，并使用一组常用的函数优化问题进行了许多测试。在IBM Beowulf pc集群上对这种并行方法进行了程序执行时间、相对速度和效率方面的性能分析

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Hybrid genetic algorithm and simulated annealing (HGASA) in global function optimization

We have implemented the sequential HGASA on a Sun Workstation machine; its performance seems to be very good in finding the global optimum of a sample function optimization problem as compared with some sequential optimization algorithms that offer low efficiency and limited reliability. However, the sequential HGASA generally needs a long run time cost. So we implemented a parallel HGASA using message passing interface (MPI) on a high performance computer and performed many tests using a set of frequently used function optimization problems. The performance analysis of this parallel approach has been done on IBM Beowulf PCs cluster in terms of program execution time, relative speed up and efficiency

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17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)

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