K-means Algorithm Based on Improved Krill Herd Algorithm and Calinski-Harabasz Index
Abstract
Aiming at the problems that the Krill Herd (KH) algorithm is easy to fall into the local optimum, the searchability is weak, and the k-means algorithm is easily affected by the selection of the initial clustering centre, a k-means algorithm based on the improved KH algorithm is proposed. The algorithm is initialized by chaos, dynamic clustering, elite leadership and random mutation strategies to improve the KH algorithm and introduce the optimal clustering number adaptive mechanism, which enhances the comprehensive optimization ability of the algorithm. Six benchmark functions test the improved KH algorithm. The effectiveness of the k-means algorithm based on the improved KH algorithm was tested and verified with UCI machine learning and artificial datasets. The verification results showed that the improved KH algorithm improved based on ensuring a faster convergence speed. Compared with other algorithms, the performance of this algorithm has been significantly improved in all aspects.
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Garcia, J., Maureira, C. (2021). A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem. Applied Soft Computing, 102, 107077.
Li, X., Guo, X., Tang, H., Wu, R., Liu, J. (2023). An improved cuckoo search algorithm for the hybrid flow-shop scheduling problem in sand casting enterprises considering batch processing. Computers & Industrial Engineering, 176, 108921.
Wang, G., Gandomi, A., Alavi, A. (2014). Stud krill herd algorithm. Neurocomputing, 128, 363-370.
Chuang, L., Hsiao, C., Yang, C. (2011). Chaotic particle swarm optimization for data clustering. Expert Systems with Applications, 38(12), 14555-14563.
Satish, G., Durga, T. (2012). Projected Clustering Using Particle Swarm Optimization. Procedia Technology, 4, 360-364.
Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D. (2004). An ant colony approach for clustering. Analytica Chimica Acta, 509(2), 187-195.
Wang, Y., Xiao, R. (2016). An ant colony based resilience approach to cascading failures in cluster supply network. Physica A: Statistical Mechanics and its Applications, 462, 150-166.
Ghezelbash, R., Daviran, M., Maghsoudi, A., Ghaeminejad, H., Niknezhad, M. (2023). Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran. Applied Geochemistry, 148, 105538.
Wang, X., Wang, J. (2014). Improved Artificial Bee Colony Clustering Algorithm Based on K-means. Applied Mechanics and Materials, 556, 3852-3855.
Gang, Y., Yang, W., Huang, X., Ma, Q., Jiang, D. (2022). Clustering of Typical Wind Power Scenarios Based on K-Means Clustering Algorithm and Improved Artificial Bee Colony Algorithm. IEEE Access, 10, 98752-98760.
Lewandowski, S., Ullrich, A. (2023). Measures to reduce corporate GHG emissions: A review-based taxonomy and survey-based cluster analysis of their application and perceived effectiveness. Journal of Environmental Management, 325, 116437.
Li, J., Tang, Y., Hua, C., Guan, X. (2014). An improved krill herd algorithm: Krill herd with linear decreasing step. Applied Mathematics and Computation, 234, 356-367.
Deng, Z., Yang, J., Dong, C., Xiang, M., Qin, Y., Sun, Y. (2022). Research on economic dispatch of integrated energy system based on improved Krill Herd algorithm. Energy Reports, 8, 77-86.
Aakanksha, S., Chandramani, K., Siddhartha, P., Surbhi, B., Kuljeet, K., Hassan, M. (2021). Spam message detection using Danger theory and Krill herd optimization. Computer Networks, 199, 108453.
Niu, P., Chen, K., Ma, Y., Li, X., Liu, A., Li, G. (2017). Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowledge-Based Systems, 118, 80-92.
Jia, H., Taheri, B. (2021). Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm. Energy Reports, 7, 3328-3337.
Moussa, M., Hadi, A., Niloufar, M. (2021). Training fuzzy inference system-based classifiers with Krill Herd optimization. Knowledge-Based Systems, 214, 106625.
Maulik, U., Sanghamitra, B. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on pattern analysis and machine intelligence, 24(12), 1650–1654.
Yan, X., Zhu, Y., Zou, W., Wang, L. (2012). A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing, 97, 241-250.
Surjanovic, S., Bingham, D. (2013). Virtual Library of Simulation Experiments: Test Functions and Datasets. Retrieved Jan 13, 2023, from http://www.sfu.ca/~ssurjano.
Markelle, K., Rachel, L., Kolby, N. (2023). The UCI Machine Learning Repository. Retrieved Feb 10, 2023, https://archive.ics.uci.edu.
Published
2023-09-01
How to Cite
ENG AIK, Lim; ABU, Mohd Syafarudy; CHOON, Tan Wee.
K-means Algorithm Based on Improved Krill Herd Algorithm and Calinski-Harabasz Index.
International Journal of Advanced Research in Technology and Innovation, [S.l.], v. 5, n. 3, p. 8-20, sep. 2023.
ISSN 2682-8324.
Available at: <https://myjms.mohe.gov.my/index.php/ijarti/article/view/23215>. Date accessed: 16 sep. 2024.
Section
Articles
