机电产品系统概念设计中多目标与多学科优化方法研究

发布时间：2018-05-09 03:26

本文选题：机电系统设计 + 多目标优化　；参考：《浙江大学》2016年博士论文

【摘要】：随着现代机电产品复杂程度的不断提高,针对多域机电产品在系统概念形成阶段的设计优化逐渐成为关注的研究热点。其主要困难是在产品系统的概念形成阶段往往涉及到多个相互耦合的学科,且设计的目标并不单一,而是以多个目标互相冲突的形式存在。为得到满意的解决方案,产品系统的概念设计必须反复迭代,为此实现设计与优化间的无缝高效集成也至关重要。目前,尽管已经有一些相关研究工作,但还存在诸多不足,如:(1)应用于复杂机电系统概念设计的启发式多目标优化算法效率还太低;(2)已有的多目标优化算法在处理带约束问题时缺乏良好的解决策略;(3)复杂机电产品涉及到多个学科知识,其对应的多学科优化问题通常表现出高度的内部耦合性,该特性大大增加了求解此类问题的计算代价,已有的一些解耦策略的求解性能不能满足研究人员的需要;(4)已有的支持概念设计的平台很多还是依据以往的经验进行决策,或者高度依赖外部的优化软件,并且存在交互困难和用户操作复杂的特点。为此,本论文围绕以上问题展开研究,主要工作包括:(1)基于几何结构的多目标粒子群优化算法。算法主要特点是通过利用当前Pareto前沿的几何结构来对整体种群进行牵引。先将当前Pareto前沿看作是多维空间的一组散乱点,进而拟合构造出几何参数空间,接着计算参数空间的法线以精确定位一组对应的牵引点,最后基于牵引点将种群的非前沿点朝着更优的方向进行演化。(2)针对带约束多目标优化问题的高效混合搜索模式算法。主要分为两步:①.可行域的搜索:这里主要处理优化模型中的约束条件,对约束条件进行归一化处理同时使用一种适配的差分算法;②.最优解搜索:在第一步得到的可行解域的基础上,对于每一个可行解通过其对应的局部最优精英集和全局最优精英集在搜索空间进行演化。(3)针对多学科优化的序列化部分解耦方法。主要包含三个步骤:①.通过分析学科之间的敏感度将多学科聚合为若干个子系统;②.对于每个子系统通过解耦操作保证每个子系统没有耦合环存在,进而子系统不用迭代反复求解;③.对于每个子系统进行局部优化处理,保证全局优化器的规模尺度。(4)基于模式的机电系统设计与优化集成方法。主要包括三个步骤:①.优化问题的构造。根据优化问题扩展版型提取优化变量并且定义优化目标、约束条件和相关语义信息;②.基于语义相似的优化方法自动选取。通过计算给定问题与模式库之间的语义相似度,为给出的优化问题自动选取最合适的优化方法;③.基于优化结果的设计更新。将优化结果直接反馈给出设计人员,帮助设计人员作出决策同时更新模式库。
[Abstract]:With the increasing complexity of modern electromechanical products, the design optimization of multi-domain electromechanical products in the system concept formation stage has gradually become the focus of attention. The main difficulty is that the concept of product system is often involved in a number of mutually coupled disciplines, and the design objectives are not single, but exist in the form of conflicting objectives. In order to obtain a satisfactory solution, the conceptual design of the product system must be iterated over and over again. Therefore, it is also important to realize the seamless and efficient integration between design and optimization. At present, although there have been some related research work, there are still many shortcomings. For example, the efficiency of heuristic multi-objective optimization algorithm applied to conceptual design of complex electromechanical systems is still too low. The existing multi-objective optimization algorithms lack a good solution strategy when dealing with constrained problems. Complex electromechanical products involve many disciplines. The corresponding multidisciplinary optimization problems usually exhibit a high degree of internal coupling, which greatly increases the computational cost of solving such problems. The performance of some existing decoupling strategies can not meet the needs of researchers. Many of the existing platforms supporting conceptual design are based on previous experience or rely heavily on external optimization software. And it has the characteristics of difficult interaction and complicated user operation. For this reason, this thesis focuses on the above problems. The main work includes: 1) Multi-objective particle swarm optimization algorithm based on geometric structure. The main feature of the algorithm is to use the geometric structure of the current Pareto frontier to pull the whole population. First, the current Pareto front is regarded as a group of scattered points in multidimensional space, then the geometric parameter space is constructed by fitting, and then the normal line of the parameter space is calculated to accurately locate a set of corresponding traction points. Finally, an efficient hybrid search pattern algorithm for constrained multi-objective optimization problems is proposed based on the traction point, which evolves the non-frontier points of the population towards a more optimal direction. Mainly divided into two steps: 1. The search of feasible region: this paper mainly deals with the constraints in the optimization model, and normalizes the constraints and uses a suitable difference algorithm. Optimal solution search: based on the feasible solution domain obtained in the first step, For each feasible solution, a serialization partial decoupling method for multidisciplinary optimization is proposed, which evolves in search space by its corresponding local optimal elite set and global optimal elite set. It consists of three steps: one. By analyzing the sensitivity between disciplines, the multi-discipline is aggregated into several subsystems. For each subsystem, decoupling operation ensures that there is no coupling loop in each subsystem, and then the sub-system is solved repeatedly without iteration. For each subsystem, local optimization is performed to ensure the scale of the global optimizer. (4) Mode-based electromechanical system design and optimization integration method. It consists of three steps: one. The structure of the optimization problem. The optimization variables are extracted according to the extended layout of the optimization problem and the optimization objectives, constraints and relevant semantic information are defined. The optimization method based on semantic similarity is selected automatically. By calculating the semantic similarity between the given problem and the schema library, the most suitable optimization method is automatically selected for the given optimization problem. Design update based on optimization results. The optimization results are fed back directly to the designers to help them make decisions and update the pattern library.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TH-39;TP18

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