S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, Time-series clustering–a decade review, Inf. Syst., vol. 53, pp. 16–38, 2015.
B. Yang, Y. Yang, Q. Li, D. Lin, Y. Li, J. Zheng, and Y. Cai, Classification of medical image notes for image labeling by using MinBERT, Tsinghua Science and Technology, vol. 28, no. 4, pp. 613–627, 2023.
M. Ahmed, R. Seraj, and S. M. S. Islam, The k-means algorithm: A comprehensive survey and performance evaluation, Electronics, vol. 9, no. 8, p. 1295, 2020.
E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN, ACM Trans. Database Syst., vol. 42, no. 3, p. 19, 2017.
P. Contreras and F. Murtagh, Hierarchical clustering, in Handbook of Cluster Analysis, C. Hennig, M. Meila, F. Murtagh, and R. Rocci, eds. New York, NY, USA: Chapman and Hall/CRC, 2015, pp. 124–145.
U. Maulik, S. Bandyopadhyay, and A. Mukhopadhyay, Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Berlin, Germany: Springer, 2011.
F. Wang, X. Wang, and S. Sun, A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization, Inf. Sci., vol. 602, pp. 298–312, 2022.
K. Zhu, J. Li, and H. Baoyin, Trajectory optimization of the exploration of asteroids using swarm intelligent algorithms, Tsinghua Science and Technology, vol. 14, no. S2, pp. 7–11, 2009.
W. Li, G. Zhang, X. Yang, Z. Tao, and H. Xu, Sizing a hybrid renewable energy system by a coevolutionary multiobjective optimization algorithm, Complexity, vol. 2021, p. 8822765, 2021.
L. Li, L. Jiao, J. Zhao, R. Shang, and M. Gong, Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering, Pattern Recogn., vol. 63, pp. 1–14, 2017.
W. Li, R. Wang, T. Zhang, M. Ming, and K. Li, Reinvestigation of evolutionary many-objective optimization: Focus on the Pareto knee front, Inf. Sci., vol. 522, pp. 193–213, 2020.
U. Maulik and S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recogn., vol. 33, no. 9, pp. 1455–1465, 2000.
W. Li, T. Zhang, R. Wang, B. Wang, Y. Song, and X. Li, A knee-point driven multi-objective evolutionary algorithm for flexible job shop scheduling, in Proc. 2019 IEEE Symp. Series on Computational Intelligence, Xiamen, China, 2019, pp. 1716–1722.
J. Handl and J. Knowles, Evolutionary multiobjective clustering, in Proc. 8th Int. Conf. on Parallel Problem Solving from Nature, Birmingham, UK, 2004, pp. 1081–1091.
E. Jiang, L. Wang, and J. Wang, Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks, Tsinghua Science and Technology, vol. 26, no. 5, pp. 646–663, 2021.
O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J. M. Pérez, and I. Perona, An extensive comparative study of cluster validity indices, Pattern Recogn., vol. 46, no. 1, pp. 243–256, 2013.
J. Handl and J. Knowles, Multi-objective clustering and cluster validation, in Multi-Objective Machine Learning, Y. Jin, ed. Berlin, Germany: Springer, 2006, pp. 21–47.
R. Wang, S. Lai, G. Wu, L. Xing, L. Wang, and H. Ishibuchi, Multi-clustering via evolutionary multi-objective optimization, Inf. Sci., vol. 450, pp. 128–140, 2018.
W. Li, T. Zhang, R. Wang, S. Huang, and J. Liang, Multimodal multi-objective optimization: Comparative study of the state-of-the-art, Swarm Evol. Comput., vol. 77, p. 101253, 2023.
A. Gupta, Y. S. Ong, and L. Feng, Multifactorial evolution: Toward evolutionary multitasking, IEEE Trans. Evol. Comput., vol. 20, no. 3, pp. 343–357, 2016.
W. Li, R. Wang, T. Zhang, M. Ming, and H. Lei, Multi-scenario microgrid optimization using an evolutionary multi-objective algorithm, Swarm Evol. Comput., vol. 50, p. 100570, 2019.
Y. Liu, T. Özyer, R. Alhajj, and K. Barker, Integrating multi-objective genetic algorithm and validity analysis for locating and ranking alternative clustering, Informatica, vol. 29, no. 1, pp. 33–40, 2005.
M. Kim and R. S. Ramakrishna, New indices for cluster validity assessment, Pattern Recogn. Lett., vol. 26, no. 15, pp. 2353–2363, 2005.
G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, 2nd ed. Alexandria, VA, USA: SIAM, 2007.
H. Li, X. Wu, X. Wan, and W. Lin, Time series clustering via matrix profile and community detection, Adv. Eng. Inf., vol. 54, p. 101771, 2022.
A. Blázquez-García, A. Conde, U. Mori, and J. A. Lozano, A review on outlier/anomaly detection in time series data, ACM Comput. Surv., vol. 54, no. 3, p. 56, 2021.
S. Salvador and P. Chan, Toward accurate dynamic time warping in linear time and space, Intelligent Data Analysis, vol. 11, no. 5, pp. 561–580, 2007.
J. Hu, Y. Pan, T. Li, and Y. Yang, Tw-co-MFC: Two-level weighted collaborative fuzzy clustering based on maximum entropy for multi-view data, Tsinghua Science and Technology, vol. 26, no. 2, pp. 185–198, 2021.
R. A. Armstrong, Should Pearson’s correlation coefficient be avoided? Ophthalmic Physiol. Opt., vol. 39, no. 5, pp. 316–327, 2019.
Y. Zhang, Y. Li, J. Song, X. Chen, Y. Lu, and W. Wang, Pearson correlation coefficient of current derivatives based pilot protection scheme for long-distance LCC-HVDC transmission lines, Int. J. Electr. Power Energy Syst., vol. 116, p. 105526, 2020.
S. Liu, Q. Lin, L. Feng, K. C. Wong, and K. C. Tan, Evolutionary multitasking for large-scale multiobjective optimization, IEEE Trans. Evol. Comput., .
E. Osaba, J. Del Ser, A. D. Martinez, and A. Hussain, Evolutionary multitask optimization: A methodological overview, challenges, and future research directions, Cogn. Comput., vol. 14, no. 3, pp. 927–954, 2022.
K. K. Bali, Y. S. Ong, A. Gupta, and P. S. Tan, Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II, IEEE Trans. Evol. Comput., vol. 24, no. 1, pp. 69–83, 2020.
P. T. H. Hanh, P. D. Thanh, and H. T. T. Binh, Evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest-path tree problem, Inf. Sci., vol. 553, pp. 280–304, 2021.
A. Gupta, Y. S. Ong, L. Feng, and K. C. Tan, Multiobjective multifactorial optimization in evolutionary multitasking, IEEE Trans. Cybern., vol. 47, no. 7, pp. 1652–1665, 2017.
Y. S. Ong and A. Gupta, Evolutionary multitasking: A computer science view of cognitive multitasking, Cogn. Comput., vol. 8, no. 2, pp. 125–142, 2016.
Y. Yuan, Y. S. Ong, A. Gupta, P. S. Tan, and H. Xu, Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP, in Proc. 2016 IEEE Region 10 Conf., Singapore, 2016, pp. 3157–3164.
L. Zhou, L. Feng, J. Zhong, Y. S. Ong, Z. Zhu, and E. Sha, Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem, in Proc. 2016 IEEE Symp. Series on Computational Intelligence, Athens, Greece, 2016, pp. 1–8.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
A. Mukhopadhyay, U. Maulik, and S. Bandyopadhyay, A survey of multiobjective evolutionary clustering, ACM Comput. Surv., vol. 47, no. 4, p. 61, 2015.
W. Gong, Z. Cai, and D. Liang, Adaptive ranking mutation operator based differential evolution for constrained optimization, IEEE Trans. Cybern., vol. 45, no. 4, pp. 716–727, 2015.
J. Zhang and L. Xing, An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem, Comput. Oper. Res., vol. 139, p. 105626, 2022.
R. Lu, H. Shen, Z. Feng, H. Li, W. Zhao, and X. Li, HTDeT: A clustering method using information entropy for hardware Trojan detection, Tsinghua Science and Technology, vol. 26, no. 1, pp. 48–61, 2021.
X. Li and H. Liu, Greedy optimization for K-means-based consensus clustering, Tsinghua Science and Technology, vol. 23, no. 2, pp. 184–194, 2018.
X. Chang, D. Tao, and X. Chao, Multi-view self-paced learning for clustering, in Proc. 24th Int. Conf. on Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 3974–3980.
W. Li, T. Zhang, R. Wang, and H. Ishibuchi, Weighted indicator-based evolutionary algorithm for multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 25, no. 6, pp. 1064–1078, 2021.
W. Li, X. Yao, T. Zhang, R. Wang, and L. Wang, Hierarchy ranking method for multimodal multiobjective optimization with local Pareto fronts, IEEE Trans. Evol. Comput., vol. 27, no. 1, pp. 98–110, 2023.
X. Yao, W. Li, X. Pan, and R. Wang, Multimodal multi-objective evolutionary algorithm for multiple path planning, Comput. Ind. Eng., vol. 169, p. 108145, 2022.