基于改进免疫遗传算法的桥式起重机主梁优化设计

发布时间：2018-06-14 11:19

本文选题：桥式起重机 + 遗传算法　；参考：《中北大学》2014年硕士论文

【摘要】：桥式起重机是国家经济发展建设中不可或缺的、应用广泛的重要起重设备。桥架结构中的主梁是桥式起重机的重要结构部件，研究对其进行结构优化的方法，使其减轻重量，不但能够在很大程度上节省资金，具有很强的实用价值，而且对于起重机械的发展也具有重要的意义。遗传算法是近年来发展迅速应用广泛的一种优化算法，但在解决工程优化问题时，常存在早熟收敛、容易陷入局部最优解等问题。因此本文在研究了遗传算法与人工免疫算法相关理论的基础上，对两种算法的优点加以利用，将两种算法结合，提出了一种对遗传算法的改进算法——自适应免疫遗传算法。以遗传算法为基本框架，将人工免疫算法的算子和选择方法融合进去。利用自适应概念，创新地设计了自适应交叉概率和变异概率的公式，相比免疫遗传算法的算子取值固定，自适应概念的引入可以对寻优过程进行智能调节，提高了寻优速度以及增加了抗体种群的多样性。该算法的提出，为基本遗传算法的一些缺点，，提出了改进的方法。将该混合算法应用于对桥式起重机的正轨箱形主梁的截面尺寸优化。优化结果表明，优化后主梁截面面积较原模型减小了13.6%，具有较明显的效果，达到了节省钢材，减轻重量的目的。为了验证优化模型的合理性，本文将优化后的模型导入ANSYS Workbench进行分析求解，得到的数据验证了算法应用于桥式起重机主梁的可行性。
[Abstract]:Bridge crane is an indispensable and widely used lifting equipment in the national economic development. The main beam of the bridge frame structure is an important structural part of the bridge crane. To study the method of structure optimization to reduce its weight can not only save money to a great extent, but also have a strong practical value. And for the development of lifting machinery is also of great significance. Genetic algorithm (GA) is one of the most widely used optimization algorithms in recent years. However, in solving engineering optimization problems, there are problems such as premature convergence, easy to fall into local optimal solution and so on. Therefore, on the basis of studying the theory of genetic algorithm and artificial immune algorithm, this paper makes use of the advantages of the two algorithms, and puts forward an improved genetic algorithm, adaptive immune genetic algorithm, by combining the two algorithms. Based on genetic algorithm (GA), the operator and selection method of artificial immune algorithm (AIA) are fused. By using adaptive concept, the formulas of adaptive crossover probability and mutation probability are designed innovatively. Compared with the fixed operator value of immune genetic algorithm, the introduction of adaptive concept can intelligently adjust the optimization process. The optimization speed is improved and the diversity of antibody population is increased. For some shortcomings of the basic genetic algorithm, an improved method is proposed. The hybrid algorithm is applied to optimize the cross section size of the box girder on the right track of the bridge crane. The optimization results show that the cross-section area of the optimized main beam is reduced by 13.6than that of the original model, which has obvious effect and achieves the purpose of saving steel and reducing the weight. In order to verify the rationality of the optimization model, the optimized model is imported into the ANSYS Workbench to analyze and solve, and the data obtained verify the feasibility of applying the algorithm to the bridge crane girder.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TH215

【引证文献】