A Problem Solving Environment for Combinatorial Optimization Based on Parallel Meta-heuristics

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI:10.1109/CIRA.2007.382922

Rong Huang, Shurong Tong, W. Sheng, Zhun Fan

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引用次数: 1

Abstract

Computational grid offers a great potential solution to parallel meta-heuristics toward combinatorial optimization. However, it is quite difficult for specialists in combinatorial optimization to develop parallel meta-heuristics in extremely heterogeneous computational environment, starting from scratch without any toolkit. This paper presents a problem solving environment for combinatorial optimization based on parallel meta-heuristics (PSEPMH) to help specialists to harness heterogeneous computational resources and handle dynamic granularity control. PSEPMH requires specialist to decompose one problem into two sub-problems with divide-and-conquer framework just as generic sequential algorithm. Then compiler of PSEPMH generates mobile agent code that automatically forms adaptive multi-granularity parallel computing at runtime by cloning himself and distributing along dynamic, complex grid environment with the support of PSEPMH. Not only can PSEPMH relieve specialists' burden, but also make use of the computational resources more efficiently.

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基于并行元启发式的组合优化问题求解环境

计算网格为并行元启发式组合优化提供了一个很有潜力的解决方案。然而，对于组合优化专家来说，在极端异构的计算环境中开发并行元启发式是相当困难的，没有任何工具包。本文提出了一种基于并行元启发式(PSEPMH)的组合优化问题求解环境，以帮助专家利用异构计算资源和处理动态粒度控制。PSEPMH需要专家将一个问题分解成两个子问题，就像一般的顺序算法一样，采用分治框架。在PSEPMH的支持下，编译器生成运行时自动形成自适应多粒度并行计算的移动代理代码，并在动态复杂的网格环境中进行自我克隆和分布。PSEPMH不仅可以减轻专家的负担，而且可以更有效地利用计算资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2007 International Symposium on Computational Intelligence in Robotics and Automation

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