【作者】 张仲广；

【导师】 邵之江；

【作者基本信息】 浙江大学 ， 系统工程， 2004， 硕士

【摘要】 优化技术是一门重要的科学分支，在许多工程领域得到推广和应用。然而工程中实际的优化问题，尤其是大规模复杂系统的优化问题，它们存在的求解难点之一是问题规模大，导致计算费时。一些典型的组合优化问题已被证明属于NP完全问题，对这些问题的求解，除构造近似算法求解外，也迫切的需要提高计算能力。 另一方面，优化计算的用户也需要从软硬件维护的日常琐碎事务中脱身出来，从而更专注于优化问题自身。本文立足于这些现实需求，考虑优化计算的并行化和网络化发展趋势，在对国际上现有研究成果及技术发展进行比较的基础上，结合它们的优点，提出基于分布式系统的优化计算服务。在此基础上，进行了细致的分析设计并加以实现。 本文的主要研究工作包括： 1)讨论了优化计算的并行化和网络化趋势，对现有研究成果进行了分析，并指出了其中的优缺点。 2)论述了优化计算服务的背景和需求，在比较各种并行处理系统后，提出利用集群和网格等两类分布式系统来构建优化计算服务的计算平台，分析了它们相对于其它并行处理系统的优势。 3)设计优化计算服务的各层次结构以形成完整的架构，并在集群和网格上进行详细设计和部署的说明，最终在集群上实现。多个数值实验表明优化计算服务是可行和有效的。更多还原

【Abstract】 Optimization of complex systems is often characterized by large scale, complex constraints, high nonlinearity and multiple local optima. Lack of powerful computing resource is always a key difficulty, which prevents analyzing and solving those complex problems successfully for practical use. During the last two decades, parallel processing and parallel optimization techniques have been developed to take full advantage of computing resources from different sources, either on a mainframe computer or on network of workstations. Some effective approaches have been proposed, mostly designed for a certain class of special problems. Generalization and standardization is seldom touched upon. In most cases the scarcity of a corresponding supporting environment limits the parallel optimization scheme be easily implemented and widely used. On the other hand, it is desired that users concentrate only on describing and solving optimization problems, not on trivial maintenance for software and hardware.The main contributions of this dissertation are as follows:1) The trends of parallelization and, distributed computation for optimization field are reviewed, and some leading projects are outlined and compared, with focus on their functionality and architecture.2) A new concept of optimization service is proposed. Background and system analysis is presented. Two kinds of distributed systems, clusters and grid computing systems, are chosen as the computing systems, because of their high scalability and performance/cost ratio to other kinds of parallel computing systems.3) A cross-platform system MetaSolver is designed and prototyped to verify the idea of optimization service. Aiming at solving complex and large-scale optimization problems, MetaSolver is designed to run on clusters and grid computing systems. Multiple solvers and multiple tasks could be scheduled and executed cooperatively and interactively. Framework of MetaSolver is given and its implementation is detailed. Numerical experiments on solving Rastrigin’s function and a difficult distillation column optimization problem demonstrates its effectivity.更多还原