Particle swarm optimization algorithm with multidimensional asynchronism and stochastic disturbance

Chen, Junyan; Qi, Ershi; Liu, Liang

doi:10.3724/sp.j.1087.2009.03267

Cited by 6 publications

(9 citation statements)

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“…We marked the three best results for each membership function. The most appropriate results based on the RMSE, minimum and maximum error include our proposed method (PSO-Kmeans (DBI, MSE)), PSO (DBI, MSE), methods [36] and [40], respectively. By the integration of DBI (well-separated cost) and MSE (local optimization cost), PSO could considerably improve the results in detection phase.…”

Section: Results Of Detection Phasementioning

confidence: 99%

“…The feasibility and efficiency of proposed system in training phase compared to nine different approaches. Table 4 depicts the final results using K-means, PSO (MSE), PSO (DBI, MSE), PSO-Kmeans (MSE), methods [36], [37], [38], [39], [40], and our proposed method as PSO-Kmeans (DBI, MSE). The proposed training phase outperforms other methods based on the optimal results as DR = 100%, FPR = 1.847% and Fmeasure = 98.99 %.…”

Section: Discussionmentioning

confidence: 99%

“…The computational complexity of evaluating the cost function depends on the particular cost function under consideration. The applied cost functions in preex- Figure 11: ROC curves corresponding to the proposed method and other applied methods for 2nd scenario (fuzzy approach) Figure 12: ROC curves corresponding to the proposed method and other applied methods for 2nd scenario (non-fuzzy approach) isting methods ( [36,37,38,39,40]) are O(N · K), where N is the number of data samples and K is the number of clusters. The computational order of K-means algorithm is O(T ·N ·K), where T is the number of iterations.…”

Section: Computational Ordermentioning

confidence: 99%

“…We compared and evaluated the training phase of our proposed method with standalone PSO and K-means algorithms as well as preexisting methods from the literature as [36], [37], [38], [39], and [40] which used different parameters and cost functions. We also employed both MSE and DBI criteria on all evaluations.…”

Section: Performance Measurementmentioning

confidence: 99%

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A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

2015

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In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges -from Denial of Service (DoS) to privacy attacks-will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into different categories. However, it suffers from the local convergence and sensitivity to selection of the cluster centroids. In this paper, we present a novel fuzzy anomaly detection system that works in two phases. In the first phase -the training phase-we propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase -the detection phase-we employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed algorithm can achieve to the optimal number of clusters, well-separated clusters, as well as increase the high detection rate and decrease the false positive rate at the same time when compared to some other well-known clustering algorithms.

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Section: Results Of Detection Phasementioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Computational Ordermentioning

confidence: 99%

Section: Performance Measurementmentioning

confidence: 99%

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A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

2015

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“…For instance, Simulated Annealing [19], Particle Swarm Optimization [20,21,22,23], Tabu Search [24,25], Harmony Search [26,27,28], Bees algorithm [29,30,31], and Ant Colony Optimization [32,33]. However, there is no research to solve the problem of automatically choosing input parameters in DBSCAN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Choosing DBSCAN Parameters Automatically using Differential Evolution

Karami¹,

Johansson²

2014

IJCA

105

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Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DB-SCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

Serapião

Corrêa

Gonçalves

et al. 2016

Applied Soft Computing

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a b s t r a c tData clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K-Means and on K-Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS). The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS-SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.

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Particle swarm optimization algorithm with multidimensional asynchronism and stochastic disturbance

Cited by 6 publications

References 1 publication

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

Choosing DBSCAN Parameters Automatically using Differential Evolution

Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

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