Particle swarm optimization algorithm with environmental factors for clustering analysis

Song, Wei; Ma, Wei; Qiao, Yingying

doi:10.1007/s00500-014-1458-7

Cited by 25 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This task can be achieved by employing a suitable similarity function that should be maximized/minimized the similarity between the documents clusters [6]. Several researchers have used metaheuristic optimization algorithms to solve the text clustering problem such as Genetic Algorithm [22,23], Particle Swarm Optimizer algorithm [24,25], Cuckoo search [26], Ant colony optimization [27], Artificial bee colony algorithm [28,29], Firefly algorithm [30], Harmony Search [31], and the hybrid metaheuristic approaches [32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Solving text clustering problem using a memetic differential evolution algorithm

et al. 2020

View full text Add to dashboard Cite

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Solving text clustering problem using a memetic differential evolution algorithm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To evaluate the effectiveness of the PSO-CP, its performance is compared with those of the standard particle swarm optimization (PSO) [49], fitness-distance ratio-particle swarm optimization (FDR-PSO) [53], fitness-Euclidean ratio-particle swarm optimization (FER-PSO) [42], starling particle swarm optimization (SPSO) [12], and environment particle swarm optimization (EPSO) [13] on the ten test datasets. Although there are many other advanced PSO clustering approaches, the ones used for comparison are the major references for the development of, and are more relevant to, the PSO-CP.…”

Section: Comparison With Other Clustering Approachesmentioning

confidence: 99%

“…e collective information of the neighbors is used to replace the local information in history, and subpopulations are generated when the premature phenomenon appears. Song et al [13] proposed an improved PSO procedure based on the features of the clustered data. An environment factor is added to the velocity adjustment in PSO to improve the global searching ability, which is represented by the cluster centers of the partitioning results.…”

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

Liu

Sun

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

show abstract

“…Therefore, they can easily become trapped in local optima due to the unknown shapes of search spaces. Given that K-means is a local search area method and TDC is formulated as an optimization problem [4], optimization methods that can escape the local optima can be utilized for TDC [5].…”

Section: Introductionmentioning

confidence: 99%

Text documents clustering using modified multi-verse optimizer

Abasi

Khader

Al-Betar

et al. 2020

IJECE

View full text Add to dashboard Cite

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the ﬁnal results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce signiﬁcant results in comparison with three well-established methods.

show abstract

Particle swarm optimization algorithm with environmental factors for clustering analysis

Cited by 25 publications

References 30 publications

Solving text clustering problem using a memetic differential evolution algorithm

Solving text clustering problem using a memetic differential evolution algorithm

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Text documents clustering using modified multi-verse optimizer

Contact Info

Product

Resources

About