基于数据划分的海量数据查询优化研究与应用

针对海量数据查询效率低的问题,在比较和分析了多种海量数据查询优化解决方案的优缺点后,提出了一种基于数据划分的海量数据查询性能优化方法.该方法利用多数据库处理、表分区、分表技术将数据在三个维度上将数据划分存储,减少了海量数据的查询规模.经过实验该方法提高了大规模海量数据的查询效率.     

基于ORACLE数据库的SQL应用优化

随着我国信息化建设在各行各业中不断的深入和发展,应用管理系统的性能在企业信息化中越来越被人所关注,尤其对于一些存在海量数据处理和快速响应需求的企业显得尤为重要。本文阐述了基于ORACLE数据库系统的数据库性能优化和调整的原理和相关技术。从而验证了这些优化技术在数据库性能调整和优化中所起的重要作用。通过对数据库系统的性能进行调整和优化,大大提升了对资源的使用效率,进而加快了应用系统的运行速度,对于现有系统正常有效的运行有着较高的实用价值。     

Oracle数据库性能优化技术

Oracle数据库足当前应用最广泛的大型数据库之一，而其性能优化直接关系到系统的正常运行效率。该以数据库性能优化的基本原则为出发点，阐述了在数据库设计阶段如何避免竞争和如何优化数据访问，在数据库运行阶段如何从操作系统和数据库实例级别上调整内存和I/O来达到数据库性能优化的各种技术。     

Oracle 10g数据库海量数据分页查询优化

Oracle数据库中海量数据分页查询效率直接关系到应用系统的执行效率。介绍Oracle 10g数据库中一种优化的海量数据分页查询解决方案,分析了传统分页查询技术的不足,将数据库优化策略、SQL语句优化、游标变量、批绑定、动态SQL等多种技术融为一体,并提供一个可以共享的、优化的存储过程,提高了海量数据的分页查询效率。     

一种基于存储过程的高效空间可视化检索算法

现有的GIS空间数据管理引擎对海量遥感影像数据库的数据管理和空间可视化检索存在效率低下的问题，该文在深入研究海量遥感影像数据库的空间可视化检索特点的基础上，提出了一种基于关系型数据库存储过程实现的高效的海量遥感影像数据库空间可视化检索算法，并对算法进行了多个级男日的性能优化。最后实验结果说明该算法具有高效性、稳定性和普适性。     

Oracle数据库性能优化技术在邮政系统中的应用

Oracle数据库在邮政行业应用越来越广泛,而其性能优化直接关系到邮政应用系统的运行效率。本文以数据库性能优化的基本原则为出发点,阐述了在数据库设计阶段如何避免竞争和如何优化数据访问,在数据库运行阶段如何从操作系统和数据库实例级别上调整内存和I/O来达到数据库性能优化的各种技术。     

基于实时同步的分页缓存及分区存贮的海量数据查询优化研究

随着海量数据的集中出现,对数据中心的海量数据的组织、查询和存取日益成为影响其性能的关键因素。传统的数据库优化技术只能实现降低查询处理时间或减少存储以及维护开销中的某一个或者某几个,无法达到同时优化的效果。提出一种基于实时同步的分页缓存及分区存贮（DBMS-Cache-DCS）的海量数据查询优化方法,实验结果表明,通过该方法可以同时降低访问处理时间。     

Oracle数据库性能优化技术在邮政系统中的应用

Oracle数据库在邮政行业应用越来越广泛,而其性能优化直接关系到邮政应用系统的运行效率.本文以数据库性能优化的基本原则为出发点,阐述了在数据库设计阶段如何避免竞争和如何优化数据访问,在数据库运行阶段如何从操作系统和数据库实例级别上调整内存和I/O来达到数据库性能优化的各种技术.     

基于SQL数据库的性能优化问题分析

随着数据库应用系统中数据的增加,效率是将是数据库应用开发与管理过程中必须解决的主要问题。因此必须对数据库进行优化。其中既包括了对逻辑数据库设计的优化、物理数据库硬件的优化、服务器硬件平台性能的优化、以及SQL语句本身查询的优化。本文通过对SQL Server数据库应用中影响效率的主要问题进行相应的分析,给出了数据库的性能优化方法,从而提高了整个数据库的运行效率。     

Sybase数据库性能优化研究

对运行于SCO UNIX操作系统上的Sybase数据库管理系统的优化进行研究。大型数据库性能的优化是实现高效率数据操作的基础,以双机双工计算机系统为运行环境,研究了Sybase数据库管理系统在运行环境层、服务器层、数据库层、应用层等方面的性能优化解决方案,以实际的应用为背景,分析讨论了Sybase数据库性能优化的原理和方法。实践证明,经过优化的Sybase数据库可提高约20%-30%的运行效率,取得了良好的应用效果。     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

A New Subdivision Algorithm for the Bernstein Polynomial Approach to Global Optimization

In this paper,an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems.The proposed algorithm is based on the Bernstein polynomial approach.Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point,modified rules for the selection of the subdivision direction,and a new acceleration device to avoid some unnecessary subdivisions.The performance of the proposed algorithm is numerically tested on a collection of 16 test problems.The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics.