Abstract
In the 21st century, the world will enter the global knowledge economy era, computer technology, communications technology, network technology as the representative of modern information technology has become the world's most advanced productive forces. The manufacturing sector as the country's important industries, to mention sheet material utilization, reduce costs as the core, its plate nesting in the optimization and simulation, as the era of progress and the development of technology, has made significant achievements.
The so-called sheet nesting optimization, is the use of certain specifications sheet, for a specified size, the number of specified materials and spare parts under the premise of convenience to meet cutting, so that the maximum utilization of wood strip.
Since the 20th century, with the rapid development of industrial technology and resources to the development of the growing tensions, all sectors of the economy gradually attention. People began to consider the "news" this aspect as much as possible so that resources can be fully utilized. The topics for manufacturing industry in the presence of the markers, in particular the steel nesting optimization, conducted in-depth analysis.
This paper is based on genetic algorithms to optimize the material of steel on to the next, genetic algorithm is the simulation of Darwin's natural selection of genetic selection and biological evolution of the model. It stems from the idea of genetics and survival of the fittest laws of nature, is a "survival + detection," the iterative process of the search algorithm.
The purpose of this paper is a reasonable mode, the cutting of steel making raw materials of steel materials at least the remainder, according to that intended to establish lines of programming model, with a restrictive conditions. And then use genetic algorithm toolbox simulation, and every generation to be the best function and value of the average graphics.
Keywords: genetic algorithm optimization nesting fitness function
目 录
摘要……………………………………………………………………………………I
Abstract………………………………………………………………………………II
第1章 前言
1.1国内外技术发展趋势…………………………………………………………1
1.2仿真技术的发展………………………………………………………………2
1.3课题的目的、意义及理论与技术经济价值…………………………………3
1.4 系统的总体设计 ……………………………………………………………3
第2章 遗传算法及在优化设计中的应用
2.1 遗传算法介绍…………………………………………………………………4
2.1.1遗传算法的产生………………………………………………………4
2.1.2遗传算法的发展 ………………………………………………………4
2.1.3遗传算法的特点 ………………………………………………………6
2.1.4 遗传算法的应用领域…………………………………………………7
2.1.5遗传算法的基本思想 …………………………………………………8
2.2遗传算法解法 …………………………………………………………………10
2. 2. 1生物遗传概念在遗传算法中的对应关系……………………………10
2. 2. 2最优化问题的解表示结构……………………………………………10
2.2.3处理约束条件…………………………………………………………11
2.3遗传算法的处理方法 …………………………………………………………11
第3章 遗传算法工具箱的应用
3.1 Matlab工具箱简介 …………………………………………………………13
3.2 Matlab工具箱的特点 ………………………………………………………13
3.3MATLAB 环境下遗传算法优化工具箱的应用…………………………………15
3.4 优化实例 ……………………………………………………………………16
第4章 基于遗传算法的钢材优化下料的模型的建立
4.1钢材下料优化的数学模型. …………………………………………………19
4.2 一维下料方案遗传算法的求解过程………………………………………19
4.2.3 终止准则……………………………………………………………20
第5章 遗传算法在钢材下料优化上的应用
5.1下料优化实例…………………………………………………………………21
5.2基于遗传算法的模型求解 ……………………………………………………21
第6章 结 论
致谢
参考文献
