【作者】 葛培明；

【导师】 陈虬；

【作者基本信息】 西南交通大学 ， 固体力学， 2006， 博士

【摘要】 进化计算,作为一种新兴的强大的智能优化技术,已经广泛地应用在工程科学的几乎所有的领域。与传统优化方法相比,进化计算在全局优化、复杂设计区域、复杂目标函数及易用性等方面都显示出了其优越性。遗传算法是进化计算中最重要的算法之一,本文主要研究了改进的遗传算法及其在工程优化领域中的应用。 本文的主要研究内容分章介绍如下: 第一章首先简要介绍了进化计算的基本知识,在工程优化领域的历史与研究现状。本章的最后给出了本论文的基本框架。 第二章介绍了遗传算法的基本理论与方法。首先给出了遗传算法的基本流程,然后介绍了染色体的编码方法及遗传算子,最后分析了遗传算法的搜索机理与收敛性。 第三章提出了一种新的遗传算法——基于子域搜索的遗传算法(简称为SBGA)。SBGA将设计区域分成多个小的子域,根据已搜索过的样本点在这些子域内的分布情况来指导后续的搜索。同时,SBGA还提供了一种新的处理约束的方法。应用SBGA进行了复杂函数和杆系结构的优化,数值实验还发现它能够有效地抑制早熟现象的发生。 第四章研究了基于遗传算法的连续体结构拓扑优化问题。提出了一种新的变长的紧凑编码方式——链码编码方法,用四方向链码的组合来描述结构拓扑。以机器学习中的范例推理为原型,设计出一种基于范例推理的遗传算法。在该算法中,根据多尺度变换的原理,提出了结构拓扑的“目标向量”描述方式,从而可能定量地描述不同拓扑的相似性。研制了上述方法的计算机软件GATOCS,并利用该软件对多工况连续深梁和自行车框架进行拓扑优化,取得了较为满意的结果,从而说明了应用遗传算法进行连续体结构拓扑优化是完全可行的。 第五章研究了多目标遗传算法及其并行实现。实际的工程优化问题通常都是多目标的,而且计算量很大。因此,本文研究了并行虚拟机(高速互联机更多还原

【Abstract】 Evolutionary computation (EC), as a novel and powerful intelligent optimization technology, has been utilized extensively in almost every branch in engineering science. It has more advantages over traditional optimization methods when solving problems with global search, complex design domain and complicated target functions, and it is easier to use. Genetic algorithm (GA) is one of the most important algorithms in evolutionary computation. An extensive study of improved genetic algorithms in the context of engineering optimization design has been conducted in this dissertation.The dissertation is organized as following chapters.First, a general introduction to genetic algorithms is presented in chapter 1, and the past and recent developments in this field are briefly described. The framework of the dissertation is also figured out in the end of chapter 1.Next, theoretical aspects and implementation of genetic algorithms are focused on in chapter 2. The basic workflow of genetic algorithms is described at the beginning of this chapter, and then typical representation and genetic operators are discussed. Finally, the search mechanism and convergence are investigated in the end of the chapter.Next, a novel genetic algorithm called subdomain based genetic algorithms (SBGA) is proposed in chapter 3. In SBGA, design space is divided into many small and isolate subdomains, and distribution information in these subdomains will be traced in the process of evolution, which will be used to guide subsequent search. At the same time, SBGA also provides a new method to handle complicated constraints. The numerical results demonstrate that SBGA is effective and efficient to alleviate premature convergence.Next, GA based topology optimization methods of continuum structures are studied in chapter 4. A new compact representation called four direction chain code representation is proposed in this chapter, and case-based genetic algorithms enlightened by machine learning are developed, in which a new concept named "Target Vector" is utilized to calculate the distance between two structures withdifferent topology. The expected results are obtained when applying above approach to continuum beam and bike framework topology optimization.Next, practical engineering optimum problems are of more than one target functions and computational cost is very high, so multiobjective optimization methods are discussed in chapter 5. To improve computing performance, a parallel version of improved strength evolutionary algorithms (SPEA2) is presented to optimize beam topology with two conflicting targets on the environment of cluster of workstations connected with each other.Next, a hybrid genetic algorithm is proposed which combine niche and local search techniques in chapter 6. "Mutual Information" is introduced to calculate the matching degree of two medical images from different modalities and very good results are obtained.Finally, a summary of the research conclusions, a list of innovation points and a discussion on the most promising paths of future research are also presented in chapter 7.更多还原