基于群体智能优化算法的聚类分析研究
发布时间：2019-05-27 02:56
【摘要】：聚类分析为非监督分类的一种,是将一组数据或对象通过某种特定规则分成不同类的过程。跟监督分类相比,虽然没有较高的分类精度,但是不需要先验知识,是数据或对象内部之间的聚合,在实际应用中能够得到更好的应用。但是,由于聚类分析问题初始聚类中心敏感,且容易陷入局部最优,针对此问题,许多改进方法被提出,群体智能优化算法的聚类分析是其中重要的研究方向,群体智能优化算法的聚类分析是通过将聚类问题归结为一个优化问题,然后通过全局并行搜索方式进行启发式搜索。本文主要的研究重点是群体智能优化算法的聚类分析问题,通过对常用的聚类分析方法和经典的群体智能优化算法聚类分析方法进行分析,研究其过程以及存在问题,针对聚类分析初始聚类中心敏感的问题,提出基于两种新型群体智能优化算法的聚类分析,即基于烟花算法的聚类分析和基于混合编码方式的聚类分析,并通过其与传统聚类分析方法和经典群体智能优化算法聚类分析方法的比较,评价其性能。基于烟花算法的聚类分析,是将烟花算法这种新型的智能优化算法应用到聚类分析中,该算法将两种搜索策略不同的烟花算法进行结合,分别采用实数编码和二进制编码的方式,提出了基于两种编码方式的烟花聚类算法,并通过仿真实验,对两种不同编码方式的算法性能进行分析,通过实验得出二进制烟花算法的聚类分析聚类效果好、稳定性高,并且分类精度高于经典的群体智能优化算法的聚类分析。基于混合编码方式的聚类分析,是将基于聚类中心的编码方式和基于样本编号的编码方式混合,并且在不同编码方式下分别采用QPSO和改进的雨林算法进行聚类分析。通过仿真实验得出,使用聚类中心的编码方式,在搜索过程中,容易产生超出搜索空间的解,从而使搜索陷入局部最优。而用样本编号的编码方式,搜索空间范围固定,虽然便于控制搜索范围,但是限制了搜索空间的范围,不利于进一步提高最优解的质量。基于混合编码方式的聚类分析算法,既解决了超出搜索空间问题,同时能够保持种群多样性,并且通过实验比较,分类精度优于传统的聚类分析方法。
[Abstract]:Cluster analysis is a kind of unsupervised classification, which is the process of dividing a group of data or objects into different classes through a specific rule. Compared with supervised classification, although there is no higher classification accuracy, but there is no need for prior knowledge, it is an aggregation between data or objects, and can be better applied in practical applications. However, because the initial clustering center of clustering analysis problem is sensitive and easy to fall into local optimization, many improved methods are proposed, and clustering analysis of swarm intelligence optimization algorithm is one of the important research directions. The clustering analysis of swarm intelligence optimization algorithm is based on the clustering problem, which is reduced to an optimization problem, and then heuristic search is carried out by global parallel search. The main research focus of this paper is the clustering analysis of swarm intelligence optimization algorithm. Through the analysis of the commonly used clustering analysis methods and the classical swarm intelligence optimization algorithm clustering analysis method, the process and existing problems are studied. In order to solve the problem of sensitivity of initial clustering center in clustering analysis, clustering analysis based on two new swarm intelligence optimization algorithms is proposed, that is, clustering analysis based on fireworks algorithm and clustering analysis based on hybrid coding. The performance of the method is evaluated by comparing it with the traditional clustering analysis method and the classical swarm intelligence optimization algorithm clustering analysis method. Based on the clustering analysis of fireworks algorithm, a new intelligent optimization algorithm, is applied to clustering analysis. The algorithm combines two fireworks algorithms with different search strategies. The fireworks clustering algorithm based on two coding methods is proposed by using real number coding and binary coding respectively, and the performance of the two different coding methods is analyzed through simulation experiments. The experimental results show that the clustering effect of binary fireworks algorithm is good, the stability is high, and the classification accuracy is higher than that of classical swarm intelligence optimization algorithm. The clustering analysis based on hybrid coding is to mix the coding method based on clustering center and the coding method based on sample number, and QPSO and improved rainforest algorithm are used for clustering analysis under different coding methods. The simulation results show that using the coding method of clustering center, it is easy to produce solutions beyond the search space in the search process, which makes the search fall into local optimization. However, using the coding method of sample number, the search space range is fixed, although it is convenient to control the search space, but it limits the scope of the search space, which is not conducive to further improving the quality of the optimal solution. The clustering analysis algorithm based on hybrid coding not only solves the problem of beyond search space, but also maintains the diversity of population. Through experimental comparison, the classification accuracy is better than the traditional clustering analysis method.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18;TP311.13
[Abstract]:Cluster analysis is a kind of unsupervised classification, which is the process of dividing a group of data or objects into different classes through a specific rule. Compared with supervised classification, although there is no higher classification accuracy, but there is no need for prior knowledge, it is an aggregation between data or objects, and can be better applied in practical applications. However, because the initial clustering center of clustering analysis problem is sensitive and easy to fall into local optimization, many improved methods are proposed, and clustering analysis of swarm intelligence optimization algorithm is one of the important research directions. The clustering analysis of swarm intelligence optimization algorithm is based on the clustering problem, which is reduced to an optimization problem, and then heuristic search is carried out by global parallel search. The main research focus of this paper is the clustering analysis of swarm intelligence optimization algorithm. Through the analysis of the commonly used clustering analysis methods and the classical swarm intelligence optimization algorithm clustering analysis method, the process and existing problems are studied. In order to solve the problem of sensitivity of initial clustering center in clustering analysis, clustering analysis based on two new swarm intelligence optimization algorithms is proposed, that is, clustering analysis based on fireworks algorithm and clustering analysis based on hybrid coding. The performance of the method is evaluated by comparing it with the traditional clustering analysis method and the classical swarm intelligence optimization algorithm clustering analysis method. Based on the clustering analysis of fireworks algorithm, a new intelligent optimization algorithm, is applied to clustering analysis. The algorithm combines two fireworks algorithms with different search strategies. The fireworks clustering algorithm based on two coding methods is proposed by using real number coding and binary coding respectively, and the performance of the two different coding methods is analyzed through simulation experiments. The experimental results show that the clustering effect of binary fireworks algorithm is good, the stability is high, and the classification accuracy is higher than that of classical swarm intelligence optimization algorithm. The clustering analysis based on hybrid coding is to mix the coding method based on clustering center and the coding method based on sample number, and QPSO and improved rainforest algorithm are used for clustering analysis under different coding methods. The simulation results show that using the coding method of clustering center, it is easy to produce solutions beyond the search space in the search process, which makes the search fall into local optimization. However, using the coding method of sample number, the search space range is fixed, although it is convenient to control the search space, but it limits the scope of the search space, which is not conducive to further improving the quality of the optimal solution. The clustering analysis algorithm based on hybrid coding not only solves the problem of beyond search space, but also maintains the diversity of population. Through experimental comparison, the classification accuracy is better than the traditional clustering analysis method.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18;TP311.13
【参考文献】
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