多学科模糊满意协同优化方法及其应用

发布时间：2018-05-29 09:24

本文选题：模糊优化 + 满意度　；参考：《大连理工大学》2013年博士论文

【摘要】：在国家自然科学基金和国家863高技术发展计划的资助下,本文研究了多学科模糊满意协同优化方法,主要包括：多学科协同优化(CO)分学科(子系统)建模方法、模糊满意协同优化方法、基于全局共享变量近似子空间的协同优化方法,并将其应用于全断面岩石掘进机刀具布局优化设计中。该问题在理论研究中属于多学科设计优化方法(MDO)；在机械设计中属于复杂布局方案设计；在人工智能领域中属于人机结合问题,具有重要的理论和工程应用价值。提出了基于全局共享变量近似子空间的协同优化方法,对于学科级共享变量的可行域以超切平面法来近似,当系统级共享设计变量达到了一致性要求,系统级才进行解耦操作,在一定程度上降低学科级的分析次数,具有很好的计算精度和计算稳定性,可以有效地解决TBM刀具布局优化设计这类涉及共享变量多,耦合变量相对较少的优化设计问题。为了能在协同优化中嵌入模糊因素与领域专家的知识和经验,本文给出了模糊满意协同优化模型及算法,在学科级分析模块中增加了极角模糊规则和模糊约束最优截集水平分析。从TBM刀具布局优化设计问题的应用效果来看,有效地解决了刀具布局协同优化设计中模糊推理、模糊约束的处理问题。针对TBM刀盘布置设计中具有布局计算复杂,优化器负载重、学科领域内容交叉的特点,提出了按MDO的学科分级优化的思想,利用协同优化方法,将刀盘最优布置模型分成了两层,共包括1个系统级和4个子系统(学科)级：刀盘几何学计算子系统、刀盘力学计算子系统、刀盘质心计算子系统和破岩量计算子系统,各子系统级采用并行优化器处理,以降低问题的求解规模,提高刀具布局寻优的效率。全文的主要工作内容包括： (1)讨论了并行子空间优化、协同优化和两级集成系统三种多学科优化方法的特点和应用,并根据系统工程学原理和TBM盘刀布置问题的特点,提出利用多学科协同优化方法来解决TBM刀具布局优化这类复杂问题的思想。 (2)提出了多学科协同优化分学科优化策略、建模方法和盘刀布置方案系统级调整机制。分析了标准协同优化计算困难的原因,在此基础上提出了一种改良的协同优化方法——基于全局共享变量近似子空间协同优化。并通过实例验证该方法在求解复杂工程布局问题的可行性和有效性。 (3)提出模糊协同优化模型及算法,主要包括两类模型——对称和非对称模糊协同优化模型,前者将截集水平作为学科级的共享变量来处理,它比较适合于约束条件和优化目标具有同等重要程度的情况；后者将截集水平嵌入到学科级中,由学科级分析模块来处理,比较适合于对约束条件和优化目标具有不同的重视度情况。 (4)将本文提出的方法,应用到TBM掘进机刀具布局优化的工程实例中,具体方法包括：非对称模糊满意协同优化模型、学科级分析模块中极角模糊规则、模糊约束的最优截集水平、学科级模糊推理、极角模糊推理约束条件的构建等。本文以全断面岩石掘进机刀具布局设计为应用背景,根据MDO及模糊理论与方法,给出了多学科模糊满意协同优化方法,并在该方法中应用了近似和解耦协调机制、模糊优化、模糊推理等相关理论,具有一定的工程实用价值,本文工作期望有助于推动一类复杂工程布局优化设计理论研究的进展。
[Abstract]:With the help of the National Natural Science Foundation and the National 863 High - tech Development Program , this paper studies the multi - disciplinary fuzzy satisfaction collaborative optimization method , which mainly includes the multi - disciplinary collaborative optimization ( CO ) sub - discipline ( subsystem ) modeling method , the fuzzy satisfying cooperative optimization method , the global shared variable approximation subspace and the collaborative optimization method based on the global shared variable approximation subspace , and is applied to the optimization design of the cutter layout of the full - section rock heading machine .
It belongs to the design of complex layout in mechanical design .
In the field of artificial intelligence , it belongs to the combination of man and machine , and has important theory and engineering application value .

In this paper , a collaborative optimization method based on global shared variable approximation subspace is proposed , and the feasible region of the subject - level shared variable is approximated by the hypertangent plane method . When the system - level shared design variable meets the consistency requirement , the system - level is decoupled . It has good calculation accuracy and stability . It can effectively solve the optimization design of TBM cutter layout , which involves many shared variables and relatively few coupling variables .

In order to embed the knowledge and experience of fuzzy factors and domain experts in collaborative optimization , this paper presents a fuzzy satisfying cooperative optimization model and algorithm , which adds polar angle fuzzy rule and fuzzy constraint optimal cutting level analysis in the discipline level analysis module .

According to the characteristics of complex layout calculation , heavy load and cross - cutting in the field of TBM cutter head layout design , the author puts forward the idea of optimization of discipline grading according to MDO . By means of cooperative optimization method , the optimal layout model of cutter head is divided into two layers , including 1 system level and 4 subsystems ( discipline ) level : cutter head geometry calculation subsystem , cutter head mechanics calculation subsystem , cutter head centroid calculation subsystem and rock breaking calculation subsystem , each subsystem level is processed by parallel optimizer to reduce the solution scale of the problem and improve the efficiency of optimization of cutter layout .

The main contents of the full text include :

( 1 ) The characteristics and applications of three multi - disciplinary optimization methods of parallel subspace optimization , cooperative optimization and two - stage integration system are discussed , and the idea of using multi - disciplinary collaborative optimization method to solve the complex problems of TBM cutter layout is proposed based on the characteristics of system engineering principle and TBM disc cutter layout problem .

( 2 ) The optimization strategy , modeling method and system level adjustment mechanism of multi - disciplinary collaborative optimization are put forward . The reasons of difficulty in the calculation of standard collaborative optimization are analyzed . Based on this , an improved cooperative optimization method is proposed , which is based on the global shared variable approximation subspace .

( 3 ) A fuzzy collaborative optimization model and an algorithm are proposed , which mainly include two types of model _ symmetric and asymmetric fuzzy cooperative optimization models . The former deals with the intersection level as the shared variable of the discipline level , which is suitable for the condition that the constraint condition and the optimization goal have the same importance degree .
In the latter , the truncated level is embedded in the discipline level , which is processed by the discipline level analysis module , which is suitable for the condition that the constraint condition and the optimization target have different attention degree .

( 4 ) The method proposed in this paper is applied to the engineering example of TBM boring cutter layout optimization . The concrete method includes : the asymmetric fuzzy satisfying cooperative optimization model , the polar angle fuzzy rule in the discipline level analysis module , the optimal cutting level of the fuzzy constraint , the discipline level fuzzy inference , the construction of the polar angle fuzzy inference constraint condition , and the like .

Based on MDO and fuzzy theory and method , this paper presents a multi - disciplinary fuzzy satisfactory cooperative optimization method based on MDO and fuzzy theory and method .
【学位授予单位】：大连理工大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TP18;TH122

【参考文献】