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Improving the standard ant clustering algorithm using genetic algorithms.

机译：使用遗传算法改进标准蚂蚁聚类算法。

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This thesis presents an attempt towards the improvement of the Standard Ant Clustering Algorithm by using the techniques of Genetic Algorithms. Goals of this thesis consist of multiple phases. The world of ants consists of two types of objects: artificial ants and data items. The task of the artificial ants is to wander around the world for a set number of steps, and attempt to form clusters for each type of data items. Next, ants pick-up a data item if it believes the location cell is not of a cluster. Additionally, if an ant is carrying a data item, it is expected to drop it off when it believes it falls within a cluster. During this process, any carrying ant (an artificial ant that is carrying a data item of any type) looks at a fixed neighborhood edge length to determine clusters existence. Edge length is anticipated to be relative to the world size, and it is not determined whether a larger or smaller edge would allow a higher clustering quality. This thesis will use the techniques of genetic algorithms and attempt to make use of biologically powered methods to maximize the clustering formation and come up with the best possible clusters that eventually will result into a new algorithm we will call ACAGA..

机译：本文提出了一种利用遗传算法技术改进标准蚂蚁聚类算法的尝试。本文的目标包括多个阶段。蚂蚁世界由两种类型的对象组成：人工蚂蚁和数据项。人造蚂蚁的任务是在世界各地漫游一定数量的步骤，并尝试为每种类型的数据项形成簇。接下来，如果蚂蚁认为位置单元格不是集群的话，它们将拾取数据项。另外，如果一只蚂蚁携带一个数据项，那么当它认为它属于集群时，可以将其删除。在此过程中，任何携带的蚂蚁（携带任何类型的数据项的人工蚂蚁）都会查看固定的邻域边缘长度，以确定集群的存在。预计边缘长度与世界大小有关，并且不确定较大或较小的边缘是否会允许较高的聚类质量。本文将使用遗传算法的技术，并尝试利用生物动力方法来最大化聚类的形成，并提出可能的最佳聚类，最终将产生一种新的算法，我们将其称为ACAGA。

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作者
AlFraihi, Mohammed Hamad.;
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作者单位

California State University, Fullerton.;

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授予单位 California State University, Fullerton.;
学科 Computer science.;Bioinformatics.
学位 M.S.
年度 2014
页码 39 p.
总页数 39
原文格式 PDF
正文语种 eng
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专利

1. Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm [J] . A.L. Vizine, L. N. de Castro, E. R. Hrusch Informatica: An International Journal of Computing and Informatics . 2005,第2期

机译：致力于改进聚类蚂蚁：一种自适应的蚂蚁聚类算法
2. Optimisation of distribution networks using Genetic Algorithms. Part 2 - the Genetic Algorithm and Genetic Operators [J] . Romeo M. Marian, Lee H. S. Luong, Raknoi Akararungruangkul International Journal of Manufacturing Technology and Management . 2008,第1期

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3. Safety detection algorithm in sensor network based on ant colony optimization with improved multiple clustering algorithms [J] . Zhu Limin Safety science . 2019,第期

机译：基于蚁群优化的传感器网络安全检测算法改进多元聚类算法
4. An Image Segmentation of Fuzzy C-Means Clustering Based on the Combination of Improved Ant Colony Algorithm and Genetic Algorithm [C] . Xianyi Cheng, Xiangpu Gong Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008 . 2009

机译：改进蚁群算法与遗传算法相结合的模糊C均值聚类图像分割
5. Aspect Mining Using Multiobjective Genetic Clustering Algorithms. [D] . Bethelmy, David G. 2016

机译：使用多目标遗传聚类算法的方面挖掘。
6. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm Minimum Spanning Tree and Hierarchical Clustering in an Applied Study [O] . Saeedeh Pourahmad, Atefeh Basirat, Amir Rahimi, 2020

机译：初始簇质心的确定是否提高了K-Means聚类算法的性能？应用研究中遗传算法最小生成树和分层聚类的三种混合方法的比较
7. AutoClustering: an estimation of distribution algorithm for the automatic generation of clustering algorithms. [O] . Meiguins, Aruanda S. G., Limao, R.C., Meiguins, Bianchi S., 2012

机译：AutoClustering：自动生成聚类算法的分布算法估计。
8. Boltzmann Weighted Selection Improves Performance of Genetic Algorithms. [R] . de la Maza, M., Tidor, B. 1991

机译：Boltzmann加权选择提高了遗传算法的性能。

Improving the standard ant clustering algorithm using genetic algorithms.

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