一种蚁群优化算法的应用初探

论文编号:XXLW010 论文字数:7808,页数:16

摘要

 优化技术是一种以数学为基础，用于求解各种工程问题优化解的应用技术。随着数学科研领域的迅速发展，优化问题成了一个热门研究问题。而由于其算法的复杂性和计算量的逐渐增加，越来越多的革新算法不断产生，蚁群算法就是其中一种有效工具。本文基于这个观点，引入了一种新型的优化算法—蚁群算法来加以学习整理。在此基础上，将该方法针对就业问题进行初步应用。本文先介绍了蚁群优化算法的定义、原理及多种算法，在文献[4]中有详细介绍它的原理和算法。再对诸多算法加以对比分析，并引出了搜寻理论，最后选用一种最适合的算法，针对杭州市劳动力就业搜寻问题，对算法模型进行分析。在对搜寻时间和搜寻成本这两个指标上加以研究对比得出结果，给出结果的优劣性，最后得出结论：这种蚁群算法可以缩短成功就业搜寻时间，降低搜寻成本，能充分体现优化的效果。

关键词：优化问题  蚁群算法  搜寻理论  劳动力就业

A preliminary application for
an optimized algorithm of the ant colony theory

Abstract

 Optimization technology is a mathematical basis for solving various problems of optimization of the application of technology. With the rapid development of mathematical field of scientific research,the optimization has become a hot research issues. And because of the complexity of its algorithm and gradually increasing of the amount of computation, more and more innovative algorithms are constantly generated, the ant algorithm is one of the effective tools.This article stems from this viewpoint, introduced one kind of new optimized algorithm -ant group algorithm to come to study and the preliminary application. On this basis,we will use this method on the point of unemployment to do the initial application.We first introduced the definition 、principles 、and algorithms of the ant colony optimization in this article,there are written in detail about its principles and algorithms in the literature [4]，then we do a lot of comparative analysis, and lead to the search theory，at last we choose a most suitable method and use the example of the employment search in HangZhou to verify the analysis. In the research of its searching time and searthing cost of these two indicators ,we compared the two resuits and analyse the goods and bads .Finally we find that it has shorrten the time for a successful job search and  reduced the search for lower costs by using this ant colony algorithm.And finally reflects the optimization of the results.
 
Keywords: Optimization，Ant algorithm，Search theory，Employment

目  录
摘要 i
Abstract ii
目录 III
一、引言 1
二、 蚁群算法的原理及步骤 3
2.1 蚁群优化原理及算法描述 3
2.1.1 蚁群的自组织行为 3
2.1.2 蚁群优化算法描述 5
2.2蚁群搜寻理论和算法介绍 6
2．2.1 蚁群搜寻理论 6
2．2.2 基于搜寻理论的算法描述 7
三、 应用初探-劳动力就业指标分析模型 8
3.1劳动力就业概述 8
3.2模型建立 8
3.3模型求解及结果分析 9
四、 结束语 13
五、 致谢 14
六、参考文献 15

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