Clustering Using an Improved Krill Herd Algorithm

Qin, Li; Liu, Bo

doi:10.3390/a10020056

Cited by 12 publications

(8 citation statements)

References 29 publications

(33 reference statements)

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“…The selection of these algorithms is essential to show the strength of the combination of such algorithms in MA besides the proposed adaptive mutation operator and the modified restart phase. Moreover, for further testify the performance, the AMADE is compared with recent data clustering algorithms in the literature, including K-means [9], black hole [40], age-based particle swarm optimisation [68], dynamic shuffled differential evolution algorithm [42], the krill herd algorithm [69] and hybrid ICMPKHM [19].…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…In order to evaluate the performance of AMADE, the algorithm results are compared with well-known algorithms, such as the black hole (BH) [40], age-based particle swarm optimization (PSOAG) [68], A dynamic shuffled differential evolution algorithm (DSDE) [42], the krill herd algorithm (IKHCA) [69], hybrid of krill herd algorithm with harmony search algorithm (H-KHA) [17] and hybrid ICMPKHM [19].…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

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An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

et al. 2019

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The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Experimental Setup and Resultsmentioning

confidence: 99%

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

et al. 2019

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“…developed in 2015 show promising results. So, k‐means++ algorithm can also be optimized using nature‐inspired krill herd algorithm (Li & Liu, ). Krill herd modules are initialized using k centroids from k‐means++ algorithm.…”

Section: Methodsmentioning

confidence: 99%

Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Aggarwal

Singh

2019

Expert Systems

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Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.

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“…In 2017, Li and Liu adopted a combined update mechanism of selection operator and mutation operator to enhance the global optimization ability of the KH algorithm. ey solved the problem of unbalanced local search and global search of the original KH algorithm [44].…”

Section: Krill Herd (Kh) Algorithm and Variants Krill Herd (Kh)mentioning

confidence: 99%

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

Xin

Peng

Wei

et al. 2021

Security and Communication Networks

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As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

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Clustering Using an Improved Krill Herd Algorithm

Cited by 12 publications

References 29 publications

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

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