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Multiobjective optimum design of static and seismic-resistant structures with genetic algorithm, fuzzy logic and game theory.

机译：利用遗传算法，模糊逻辑和博弈论对静，抗震结构进行多目标优化设计。

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摘要

The goal of this research is to develop a constrained multiobjective optimization method in the form of a robust, practical, problem-independent algorithm, and to investigate the effect of multiobjective optimization on structural design.; This research studies the properties and characteristics of multiobjective optimization and solutions. Design objectives, constraints and design variables as well as their effect on structural design and behaviors are investigated. Dynamic input energy for a multidegree-of-freedom structure is formulated. A multiobjective optimization algorithm based on game theory is developed. Fuzzy set theory is introduced to construct a fuzzy constrained environment. A Pareto genetic algorithm is developed based on a simple genetic algorithm. Two new genetic algorithm operators--multiobjective fitness function and niche method--are proposed. A new genetic algorithm process--Pareto set filter--is added to genetic algorithms. Finally, a multiobjective optimization algorithm, which is robust and performs uniformly well on a broad range of problems, is developed with genetic algorithm, fuzzy logic and game theory.; Six frame structures and five truss structures have been optimally designed with static load, seismic excitation, and active control. Materials for the structures are steel, reinforced concrete, and a combination of both. Optimization goals cover weight, structural cost, displacement, strain energy, input energy, potential energy, and performance index. Constraints include stress, displacement, frequency, and ratio of story stiffness. The numerical results show that the newly developed algorithms can locate a global solution and Pareto optimum set which are usually difficult to find.
机译：本研究的目的是开发一种鲁棒，实用，与问题无关的算法形式的约束多目标优化方法，并研究多目标优化对结构设计的影响。本研究研究了多目标优化和解决方案的性质和特征。研究了设计目标，约束条件和设计变量以及它们对结构设计和行为的影响。制定了多自由度结构的动态输入能量。提出了一种基于博弈论的多目标优化算法。引入模糊集理论来构建模糊约束环境。基于简单遗传算法开发了帕累托遗传算法。提出了两种新的遗传算法算子-多目标适应度函数和小生境方法。遗传算法中增加了一个新的遗传算法过程-帕累托集过滤器。最后，利用遗传算法，模糊逻辑和博弈论，提出了一种鲁棒的，在各种问题上表现均一的多目标优化算法。通过静态载荷，地震激励和主动控制，对六个框架结构和五个桁架结构进行了优化设计。结构的材料是钢，钢筋混凝土，以及两者的组合。优化目标包括重量，结构成本，位移，应变能，输入能，势能和性能指标。约束条件包括应力，位移，频率和故事刚度比率。数值结果表明，新开发的算法可以找到通常难以找到的全局解和帕累托最优集。

著录项

  • 作者

    Li, Dan.;

  • 作者单位

    University of Missouri - Rolla.;

  • 授予单位 University of Missouri - Rolla.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 1997
  • 页码 204 p.
  • 总页数 204
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;
  • 关键词

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