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兰州交通大学毕业设计(论文)

摘 要

萤火虫算法(Firefly Algorithm,FA)是受自然界中的萤火虫通过荧光进行信息交流这种群体行为的启发演变而来。它是由剑桥大学的Xin-She Yang教授在2009年提出的，它作为一种新颖的仿生群智能优化算法，有较大的研究空间。近几十年来随着越来越多的仿生群智能算法的提出，人们对于这些算法的认识和研究也逐步加深。

本文先介绍群智能优化算法的理论概念，然后着重通过对萤火虫算法仿生原理的了解，从数学的角度对萤火虫算法进行合理的描述和过程的定义，最后编写该算法的matlab代码实现对3个峰值函数进行仿真测试，得出其测试结果。同时用遗传算法对同样的测试函数也进行仿真测试，得出其测试结果。最后通过测试结果比较萤火虫算法和遗传算法分别在对峰值函数寻优结果的精确度。在比较过程中，可以根据测试结果发现，萤火虫算法在对峰值函数的寻优结果的精确度优于遗传算法。这表明了萤火虫算法在连续空间优化的可行性和有效性，同时也表明了萤火虫算法具有良好的应用前景。 关键词： 萤火虫算法，仿生群智能优化算法，优化分析，遗传算法

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兰州交通大学毕业设计(论文)

ABSTRACT

The Firefly Algorithm (FA) is affected by the nature of the Firefly exchange of information through a fluorescence inspired this kind of crowd behavior has evolved. It is made by Xin - She Yang professor at the university of Cambridge in 2009, as a novel bionic swarm intelligent optimization algorithm, has a large research space. In recent decades as more bionic swarm intelligent algorithm is put forward, people also gradually deepen to the understanding and research of those algorithms.

First，it is introduced in this paper theoretical concepts of swarm intelligence optimization algorithm, and then emphatically through the understanding of firefly algorithm bionic principle, from the perspective of mathematical descriptions of firefly algorithm is reasonable and the definition of the process, finally ,writes matlab code of the algorithm to realize the three peak function simulation test, to test results. At the same time with the genetic algorithm on the same test function, simulation test, to test results. Finally by comparing test results of firefly algorithm and genetic algorithm in the accuracy of the optimization results of peak function respectively. In the process of comparison, according to the result of test, it can shows that the firefly algorithm on the accuracy of the optimization results of peak function is superior to genetic algorithm. It shows that the feasibility and effectiveness of firefly algorithm in the continuous space optimization, but also shows that the firefly algorithm has a good application prospect.

Keywords: firefly algorithm, The bionic swarm intelligent optimization algorithm, Optimization analysis, genetic algorithm

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兰州交通大学毕业设计(论文)

目 录

摘 要 ......................................................................................................................................... I ABSTRACT .............................................................................................................................. II 目 录 ...................................................................................................................................... III 第一章 绪论 ............................................................................................................................ 1

一、二、三、第二章 一、二、三、四、五、六、七、第三章 一、二、三、四、五、六、第四章 一、二、三、四、五、研究的背景及意义 ................................................................................................... 1 群智能优化算法的研究现状 ................................................................................... 1 本论文的内容和结构 ............................................................................................... 2 群智能优化理论 ........................................................................................................ 4

群智能优化算法的概述 ........................................................................................... 4 模拟退火算法 ........................................................................................................... 4 遗传算法 ................................................................................................................... 5 蚁群算法 ................................................................................................................... 7 粒子群优化算法 ....................................................................................................... 8 人工萤火虫群优化算法 ........................................................................................... 9 人工鱼群算法 ......................................................................................................... 11 萤火虫算法 .............................................................................................................. 13

萤火虫算法的概念 ................................................................................................. 13 萤火虫算法的国内外研究现状 ............................................................................. 13 萤火虫算法的仿生原理 ......................................................................................... 14 萤火虫算法的数学描述与分析 ............................................................................. 15 萤火虫算法的流程 ................................................................................................. 16 实现萤火虫算法的matlab代码 ............................................................................ 16 仿真实验与分析 ...................................................................................................... 22

三个测试函数的介绍 ............................................................................................. 22 FA和GA对F1(x)的仿真测试 .............................................................................. 22 FA和GA对F2(x)的仿真测试 .............................................................................. 25 FA和GA对F3(x)的仿真测试 .............................................................................. 27 测试结果分析 ......................................................................................................... 30

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