基于退火遗传算法的桥式起重机主梁优化设计及软件实现

发布时间：2018-12-26 11:29

【摘要】：通用桥式起重机是国民经济建设的重要起重设备，对我国的现代化生产和基础设施的建设起着至关重要的作用。桥式起重机的金属结构占整机重量的60%以上，沿用至今的起重机金属结构设计理论偏于保守，主要表现在安全系数过高，结构自重偏大，这样就人为造成起重机的整体尺寸和重量过于庞大。若能保证在强度、刚度、稳定性的前提下，将其重量尽可能的减小，不仅有利于运输安装，同时也会使制造成本大大降低。因此，采用合理的优化设计方法对桥式起重机的金属结构进行优化，是起重机设计中必不可少的一个环节。到目前为止，国内外不少学者对起重机金属结构的优化问题进行了研究，并取得一定的成果，概括起来所使用的方法主要有：惩罚函数法、随机方向法、复合形法、遗传算法等。然而这些方法主要在优化目标的提高上做了大量工作，未对其优化性能(优化速度和全局收敛性)进行任何探讨，而一个真正实用有效的优化方法不仅要具有好的优化结果，更应具备优良的优化性能。目前用于起重机金属结构优化设计的遗传算法，基本将问题归类为混合变量的优化问题，造成必须对优化后的设计变量进行圆整；另外，基本遗传算法均存在着早熟、收敛速度慢及全局收敛率低的缺陷。鉴于此，考虑在生产实践中，人眼所能辨识的精度及单轧钢板的公称厚度“1.”对钢板所规定的厚度尺寸，，重新定位起重机金属结构的优化设计属于约束、非线性、离散变量的优化设计问题，避免了对优化后的设计变量必须进行圆整的现象；并针对遗传算法的固有缺陷将具有较强局部搜索能力模拟退火算法融入到改进的遗传算法中，形成一种混合算法——自适应模拟退火遗传算法(Adaptive Simulated Annealing Genetic Algorithm，简称ASAGA)，由于新算法强化了各自的优点，弱化了各自的缺点，达到了行为互补，通过实例验证了其较好的优化性能。基于Visual C++6.0开发平台，以通用桥式起重机主梁截面积为目标函数，建立通用桥式起重机箱形主梁优化设计的数学模型，应用所形成的混合算法编制“起重机箱形主梁优化”软件，进行通用桥式起重机主梁截面的优化，并将优化结果与工程实例进行对比，在满足主梁所要求的各种约束条件下使优化后的主梁截面积减少了19.9%，说明应用该软件能降低起重机重量，减少钢材消耗，达到了节约成本和提高设计效率的目的。
[Abstract]:Bridge crane is an important lifting equipment in national economy construction, which plays an important role in modern production and infrastructure construction of our country. The metal structure of bridge crane accounts for more than 60% of the weight of the whole crane. The design theory of crane metal structure used up to now is conservative, which mainly shows that the safety factor is too high and the weight of the structure is too large. This artificial cause the overall size and weight of the crane is too large. If the weight can be minimized on the premise of strength, stiffness and stability, it is not only advantageous to transport and installation, but also can greatly reduce the manufacturing cost. Therefore, it is necessary to optimize the metal structure of bridge crane by means of reasonable optimization design method. Up to now, many scholars at home and abroad have studied the optimization of crane metal structure, and achieved certain results. The main methods used in this paper are: penalty function method, random direction method, compound shape method, etc. Genetic algorithm, etc. However, these methods have done a great deal of work on the improvement of optimization objectives, without any discussion of their optimization performance (optimization speed and global convergence), and a real practical and effective optimization method should not only have good optimization results. It should also have excellent optimization performance. At present, the genetic algorithm used in the optimization design of crane metal structure basically classifies the problem as the optimization problem of mixed variables, which makes the optimized design variables must be rounded. In addition, the basic genetic algorithm has the defects of premature convergence, slow convergence rate and low global convergence rate. In view of this, the accuracy of human eye recognition and the nominal thickness of single rolled steel plate are considered in production practice "1." For the thickness dimensions of steel plate, the optimization design of the metal structure of the repositioning crane belongs to the optimization design problem of constraint, nonlinear and discrete variables, which avoids the phenomenon that the optimized design variables must be rounded. Aiming at the inherent defects of genetic algorithm, the simulated annealing algorithm with strong local search ability is integrated into the improved genetic algorithm to form a hybrid algorithm-adaptive simulated annealing genetic algorithm (Adaptive Simulated Annealing Genetic Algorithm,) for short ASAGA),. Because the new algorithm strengthens their advantages and weakens their shortcomings, the new algorithm achieves complementary behavior, and its better optimization performance is verified by an example. Based on the Visual C 6.0 development platform, taking the cross section area of the main girder of the general bridge crane as the objective function, the mathematical model of the optimization design of the box girder of the general bridge crane is established. The software of "crane box girder optimization" is compiled by using the hybrid algorithm, and the optimization of the section of the main girder of the general bridge crane is carried out. The optimization results are compared with the engineering examples. The optimized cross section area of the main beam can be reduced by 19.9g under various constraint conditions which meet the requirements of the main beam. It is shown that the application of the software can reduce the crane weight and steel consumption, and achieve the purpose of saving cost and improving the design efficiency.
【学位授予单位】：太原科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH215

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