减震系统动态响应结构的遗传算法优化模型_毕业论文.doc

PAGE \* MERGEFORMAT I 摘 要 遗传算法是一种模拟达尔文的遗传选择和生物进化过程搜索最优解的方法。目前，遗传算法已经被广泛应用在机器学习、自动控制、图像处理、模式识别、优化调度、人工神经网络、通信网络、经济预测、函数优化等许多领域。遗传算法在进行一系列随机迭代、进化计算时，模拟了自然选择和种群遗传过程中发生的选择、交叉、变异现象，根据适者生存、优胜劣汰的自然法则，利用选择、交叉、变异这些操作使优良的品质被逐渐保留并加以组合，同时不断产生更佳的个体和淘汰不好的个体，通过逐代产生并优选个体，使整个群体向前进化发展，即不断接近于最优解。 本文阐述了减震系统动态响应结构的遗传算法优化模型，以某发射装置减震系统的动态响应结构为研究对象，在VS环境下使用C#语言编写了遗传算法程序对其进行了优化设计。 论文安排上，本文首先介绍了论文背景、国内外研究现状以及作者所做的工作；其次，介绍了弹体等效建模的方法；然后，详细阐述了遗传算法的基本原理；接着，分析了发射装置减震系统的动态响应结构并建立了遗传算法优化模型；最后，对所做的工作进行了总结和展望。 关键词：结构优化；遗传算法；等效建模  Abstract Genetic algorithm is an optimization method which simulates Darwins genetic selection and biological evolution process searching optimal solution. At present, genetic algorithm has been widely used in many fields such as machine learning, automatic control, image processing, pattern recognition, optimal scheduling, artificial neural network, communication network, economic forecasting, function optimization and so on. Genetic algorithm is used to simulate the selection, crossover and mutation of natural selection and population genetic process in a series of random iteration and evolutionary computation. According to the natural law of survival and survival of the fittest, Excellent quality is gradually retained and combined, while continuing to produce better individuals and eliminate the poor individuals, through generation and generation of individuals, so that the whole group evolved forward, that is close to the optimal solution. In this paper, the genetic algorithm optimization model of the dynamic response structure of the shock absorber system is described. The dynamic response structure of the vibration reduction system of a transmitting device is studied. The genetic algorithm is designed in the C# language. In this paper, the paper introduces the background of the paper, the status quo of the research at home and abroad, and the work done by the author. Secondly, the method of modeling the equivalent of the missile is introduced. Then, the basic pri