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首页> 外文学位 >Evolutionary optimization methods for high-dimensional complex systems: Theory, algorithm, and application to rainfall-runoff models.
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Evolutionary optimization methods for high-dimensional complex systems: Theory, algorithm, and application to rainfall-runoff models.

机译：高维复杂系统的进化优化方法：理论，算法及其在降雨径流模型中的应用。

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摘要

With the growth of computer capability, direct search methods for global optimization have been implemented to address a wide range of problems in science and engineering owing to their outstanding features: (1) require no mathematic modeling of the objective systems or their derivatives, (2) cope with practical difficulties such as non-convexity, discontinuity, multimodality, and (3) perform high efficiency and efficacy in practice. In particular, the last two decades have witnessed a boom of evolutionary computation, an active branch of direct search which produces a population of particles to probe the search space. Many evolutionary algorithms have been developed, catalyzed by the rapid expansion of their applications in real-world problems. On the other hand, evolutionary algorithms have been frequently unsuccessful in solving high-dimensional problems in practical applications. The solution for high-dimensional optimization remains a major challenge in research community of evolutionary computation. This dissertation is dedicated to the investigation of theoretical obstacles for evolutionary search strategy in high-dimensional spaces and the development of algorithms to break through these barriers. We have identified three major causes that are responsible for the inefficiency and/or ineffectiveness of evolution search in high-dimensional spaces: (1) the volume of the search space increases exponentially with the increase of dimensionality, which fatigues strategies relying too much on stochastic process and favors schemes making good use of information from the response surface of the objective function; (2) failure to keep the search proceeding in the full space spanned by all parameters to be optimized is not a trivial issue in high-dimensional problems and special procedures are needed to assure it; and (3) Bound violation is prevailing in high-dimensional search and therefore proper bound handling strategy is of great importance. A new strategy, SCPCA (Shuffled Complex evolution with Principal Component Analysis), is designed to deal with these difficulties. Examinations of this strategy on six sophisticated composition benchmark functions demonstrate that SCPCA surpasses the two most popular algorithms, PSO and DE, on high-dimensional problems. Applying the SCPCA strategy to parameter calibration of the National Weather Service Sacramento-Soil Moisture Account (SAC-SMA) model produces parameter values and parameter uncertainty distributions compared with the previous studies.
机译：随着计算机能力的提高，由于其卓越的功能，已经实现了用于全局优化的直接搜索方法，以解决科学和工程学中的各种问题：（1）不需要对目标系统或其派生类进行数学建模，（2 ）处理非凸性，间断性，多峰性等实际困难，并且（3）在实践中表现出很高的效率和功效。特别是在过去的二十年中，见证了进化计算的蓬勃发展，这是直接搜索的活跃分支，它产生大量的粒子来探测搜索空间。由于在现实世界中问题的应用迅速扩展，催生了许多进化算法。另一方面，进化算法在解决实际应用中的高维问题上常常是失败的。高维优化的解决方案仍然是进化计算研究领域的主要挑战。本文致力于高维空间进化搜索策略的理论障碍的研究，以及突破这些障碍的算法的发展。我们确定了导致高维空间中进化搜索效率低下和/或无效的三个主要原因：（1）搜索空间的容量随着维数的增加呈指数增长，这使疲劳策略过度依赖随机性处理和支持计划，以充分利用目标函数响应面上的信息； （2）在高维问题中，未能使搜索过程保持在要优化的所有参数所覆盖的整个空间中并不是一个小问题，需要采取特殊的程序来确保它； （3）边界违规在高维搜索中很普遍，因此适当的边界处理策略非常重要。为了应对这些困难，设计了一种新的策略SCPCA（带主成分分析的混洗复杂演化）。通过对六个复杂的成分基准功能的此策略进行的检验表明，在高维问题上，SCPCA超越了两种最受欢迎​​的算法PSO和DE。与先前的研究相比，将SCPCA策略应用于国家气象局萨克拉曼多土壤水分帐户（SAC-SMA）模型的参数校准会产生参数值和参数不确定性分布。

著录项

  • 作者

    Chu, Wei.;

  • 作者单位

    University of California, Irvine.;

  • 授予单位 University of California, Irvine.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 186 p.
  • 总页数 186
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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