swarm intelligence research

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DOI: https://doi.org/10.4109/TEVC.2013.2281535. 147154, 2021. Progress in Artificial Intelligence, vol. Swarm and Evolutionary Computation, vol. 2, no. DOI: https://doi.org/10.3969/j.issn.1000-3932.2007.03.001. DOI: https://doi.org/10.1145/3231934. A multistage evolutionary algorithm for many-objective optimization. Task routing and assignment in crowdsourcing based on cognitive abilities. K. L. Huang, S. S. Kanhere, W. Hu. 12431256, 2012. Information Sciences, vol. Neural Processing Letters, vol. DOI: https://doi.org/10.1098/rsif.2018.0130. 94, no. MathSciNet D. Hettiachchi, N. Van Berkel, V. Kostakos, J. Goncalves. DOI: https://doi.org/10.1016/j.neucom.2018.06.032. IEEE Transactions on Aerospace and Electronic Systems, vol. Neurocomputing, vol. Google Scholar. Particle swarm optimization. DOI: https://doi.org/10.1016/j.ins.2021.12.096. 7, pp. DOI: https://doi.org/10.1109/TEVC.2013.2297160. Applied Mathematics and Computation, vol. 12, pp. Part of Springer Nature. A developer recommendation framework in software crowdsourcing development. A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems. 4, pp. A cooperative multipopulation approach to clustering temporal data. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. DOI: https://doi.org/10.1109/TMAG.2018.2839663. 76, pp. 349374, 2021. DOI: https://doi.org/10.1109/TSC.2016.2616868. 19831991, 2013. 56, no. 3, pp. An ACO-based algorithm for parameter optimization of support vector machines. Z. Lin, W. F. Gao. 3, pp. DOI: https://doi.org/10.1016/j.advengsoft.2016.01.008. Distributed individuals for multiple peaks: A novel differential evolution for multimodal optimization problems. DOI: https://doi.org/10.1109/TPDS.2017.2735400. M. L. Mauldin. 1,311 sq ft Our Team of Professional Essay Writers. How social influence can undermine the wisdom of crowd effect. J. Zhao, X. L. Wang, M. Li. 343355, 2017. 9, no. Temporal context-aware task recommendation in crowdsourcing systems. 6985, 2020. To solve the proposed novel GRRAP, a new algorithm, called the BAT-SSOA3, used the simplified swarm optimization (SSO) to update solutions, the small-sampling tri-objective orthogonal array (SS3OA) to tune the parameters in the proposed algorithm, the binary-addition-tree algorithm (BAT) to calculate the fitness (i.e., reliability) of each . Dual differential grouping: A more general decomposition method for large-scale optimization. 11561168, 2021. V. Ambati, S. Vogel, J. G. Carbonell. DOI: https://doi.org/10.1163/15685373-12342163. She is currently an associate professor of College of Big Data and Intelligent Engineering at Yangtze Normal University, and she is also a postdoctoral fellow at the Chongqing University of Posts and Telecommunications, China. W. N. Wu, X. G. Wang, N. G. Cui. 3, pp. B. Chen, D. Yang, J. Q. Yu. A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem, IEEE Trans. IEEE Transactions on Cybernetics, to be published. DOI: https://doi.org/10.5555/3104482.3104628. DOI: https://doi.org/10.3969/j.issn.2095-2163.2019.01.024. Swarm Intelligence is a cutting-edge technology that involves mimicking the behavior of social insects for optimization purposes. IEEE Transactions on Cybernetics, vol. Journal of Cognition and Culture, vol. DOI: https://doi.org/10.11897/SP.J.1016.2019.01289. W. Wei, Q. Wang, H. Wang, H. G. Zhang. 37, pp. Energy, vol. 3, pp. 13051325, 2020. 34013412, 2017. Q. Wang, L. Tan. Quality-assured synchronized task assignment in crowdsourcing. The case for a reputation system in participatory sensing. 5988, 2004. In Proceedings of the 15th National Software Application Conference on Software Engineering and Methodology for Emerging Domains, Springer, Kunming, China, pp. It is based on studying collective behavior in decentralized and self-organized systems. A. Ratnaweera, S. K. Halgamuge, H. C. Watson. 9096 2017. 14831497, 2005. 111122, 2019. IEEE Transactions on Cybernetics, vol. DOI: https://doi.org/10.1109/ICCA.2019.8899987. (in Chinese). Swarm Intelligence. DOI: https://doi.org/10.3390/sym13091707. Intell. L. P. Wang, M. L. Feng, Q. C. Qiu, M. L. Zhang, F. Y. QiuSurvey on preference-based multi-objective evolutionary algorithms. S. Mirjalili, J. S. Dong, A. S. Sadiq, H. Faris. R. A. Sarker, S. M. Elsayed, T. Ray. 1, pp. 523537, 2020. He is currently a Ph. 44, no. 341359, 1997. Neurocomputing, vol. Z. J. Wang, Z. H. Zhan, S. Kwong, H. Jin, J. Zhang. The journal is intended for academics, practitioners and researchers who keen on such subjects of scientific research. IEEE Transactions on Evolutionary Computation, vol. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. DOI: https://doi.org/10.1109/TCYB.2019.2943928. DOI: https://doi.org/10.1109/TCYB.2019.2944873. DOI: https://doi.org/10.1016/j.asoc.2015.07.046. The company, best known for its credit card business, started out as a spin-off from a local bank, and when Donehey joined it in 1994, its IT . SCIENTIA SINICA Informationis, vol. 518530, 2018 DOI: https://doi.org/10.1016/j.ast.2018.06.013. 1. A. P. Lin, W. Sun, H. S. Yu, G. H. Wu, H. W. Tang. Show More Mission & Scope: 4661, 2014. In Proceedings of the 22nd International Conference on World Wide Web, ACM, Rio de Janeiro, Brazil, pp. A. Sunilkumar S. Manvi, in Recent Trends in Computational Intelligence Enabled Research, 2021. Knowledge-based Systems, vol. A constructive model for collective intelligence. 2, pp. Knowledge-based Systems, vol. Triple archives particle swarm optimization. (in Chinese). 17, pp. In Proceedings of the 3rd International Conference on Advances in Swarm Intelligence, Springer, Shenzhen, China, pp. 348, pp. T. Blackwell, J. Kennedy. Swarm intelligence techniques are population-based stochastic methods used in combinatorial optimization problems in which the collective behaviour of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Y. Yan, R. Rosales, G. Fung, J. G. Dy. Automatic carrier landing system multilayer parameter design based on Cauchy mutation pigeon-inspired optimization Aerospace Science and Technology, vol. S. Jagabathula, L. Subramanian, A. Venkataraman. Developer recommendation for crowdsourced software development tasks. IEEE Transactions on Cybernetics, vol. M. R. G. Raman, N. Somu, K. Kirthivasan, R. Liscano, V. S. S. Sriram. 24132425, 2016. 487512, 2020. Z. G. Chen, Z. H. Zhan, Y. Lin, Y. J. Gong, T. L. Gu, F. Zhao, H. Q. Yuan, X. F. Chen, Q. Li, J. Zhang. W. Z. Li, W. A. Guo, Y. M. Li, L. Wang, Q. D. Wu. Solving the dynamic weapon target assignment problem by an improved artificial bee colony algorithm with heuristic factor initialization. 23, no. Metaheuristic algorithms and probabilistic behaviour: A comprehensive analysis of ant colony optimization and its variants. Artificial Intelligence Review, vol. It inspires researchers in engineering sciences to learn theories from nature and incorporate them. IEEE Transactions on Evolutionary Computation, vol. 13, no. The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. 12, pp. Genetic algorithms for the traveling salesman problem. G. H. Wu, R. Mallipeddi, P. N. Suganthan, R. Wang, H. K. Chen. DOI: https://doi.org/10.13328/j.cnki.jos.006313. Engineering Applications of Artificial Intelligence, vol. A. Prakasam, N. Savarimuthu. 703712, 1993. 16, 2007. Abstract:Swarm intelligence is the discipline deals with artificial and natural systems that consists various individuals coordinated using self-organization and decentralized control. W. Q. Xu, C. Chen, S. X. Ding, P. M. Pardalos. 31, no. Du, Z. H. Zhan, S. Kwong, T. L. Gu, J. Zhang. A cooperative coevolutionary approach to function optimization. Research on the performance of multi-population genetic algorithms with different complex network structures. Computer Engineering and Applications, vol. Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence. Optimized group formation for solving collaborative tasks. 4, pp. Swarm intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting today the most high-growing stream on bioinspired computation community [].A clear trend can be deduced by analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has been in crescendo at a . G. Y. Wang. 1, Article number 2, 2019. DOI: https://doi.org/10.1007/978-3-642-58069-7_38. Effective resource allocation in cooperative co-evolutionary algorithm for large-scale fully-separable problems. A. Gupta, Y. S. Ong, L. Feng, K. C. Tan. 54, no. Collective intelligence based software engineering. Y. H. Ma, H. Zhang, Y. DOI: https://doi.org/10.1016/j.eswa.2007.04.017. Expert Systems with Applications, vol. 6, pp. It emphasizes research on Robot, which includes concerns such as Swarm robotics. Q. DOI: https://doi.org/10.1109/ROBOT.2005.1570636. IEEE Transactions on Evolutionary Computation, vol. DOI: https://doi.org/10.1016/j.knosys.2017.12.031. 219253, 2019. DOI: https://doi.org/10.1109/TCYB.2016.2554622. W. Shao, X. N. Wang, W. P. Jiao. Journal of Computer Applications, vol. 431451, 2010. DOI: https://doi.org/10.1109/tkde.2019.2935443. M. M. Kamel, A. Gil-Solla, M. Ramos-Carber. Multicriteria-based crowd selection using ant colony optimization. Information Sciences, vol. DOI: https://doi.org/10.1016/j.ins.2014.09.053. 62221005, 61936001 and 62006029), Natural Science Foundation of Chongqing, China (Nos. DOI: https://doi.org/10.1016/j.asoc.2020.106560. Survey of task assignment for crowd-based cooperative computing. 37, no. A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems. Impact of communication topology in particle swarm optimization. 421441, 2019. In Proceedings of IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. (in Chinese), Article 365370, 2012. 67, no. 571583, 2019. King, K. S. Leung. M. Karakoyun, A. zkis, H. Kodaz. R. Cheng, Y. C. Jin. Coordinated optimization algorithm combining GA with cluster for multi-UAVs to multi-tasks task assignment and path planning. DOI: https://doi.org/10.1109/TAES.2018.2831138. 8, pp. 1825, 2018. Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling. 33, no. 160, pp. 8, pp. 26462666, 2022. DOI: https://doi.org/10.1109/TEVC.2004.826071. 182202, 2015. 141, Article number 113449, 2021. Blending roulette wheel selection & rank selection in genetic algorithms. DOI: https://doi.org/10.1109/TEVC.2018.2868770. IEEE Transactions on Cybernetics, vol. In nature, it describes how honeybees migrate, how ants form perfect trails, and how birds flock. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. 26, pp. DOI: https://doi.org/10.1016/j.asoc.2020.106798. IEEE Transactions on Cybernetics, vol. 603622, 2019. 182197, 2002. DOI: https://doi.org/10.1109/TCYB.2020.2977956. Advances in Engineering Software, vol. 7, no. 26, no. The International Journal of Swarm Intelligence Research (IJSIR) serves as a forum for facilitating and enhancing the information sharing among swarm intelligence researchers in the field, ranging from algorithm developments to real-world applications. DOI: https://doi.org/10.1109/TEVC.2019.2944180. 526553, 2009. 43, no. 2, Article number 152316, 2021. 27152729, 2020. CEPT: Collaborative editing tool for non-native authors. Information and Software Technology, vol. Google Scholar, Y. Jiang, W. Zhang, P. Wang, X. Y. Zhang, H. Mei. J. Y. Li, Z. H. Zhan, K. C. Tan, J. Zhang. For small-sized journals, the figures should be 119 mm wide and not higher than 195 mm. A survey of evolutionary algorithms. 5, pp. 34023407, 2005. DOI: https://doi.org/10.1016/j.asoc.2021.107854. DOI: https://doi.org/10.1016/j.dss.2020.113449. X. S. Yang. Generation-level parallelism for evolutionary computation: A pipeline-based parallel particle swarm optimization. The International Journal of Swarm Intelligence Research (IJSIR) Email: jack8375@gmail.com Jeng-Shyang Pan Guest Editor The International Journal of Swarm Intelligence Research (IJSIR) Email: jengshyangpan@gmail.com Pei-Wei Tsai Guest Editor The International Journal of Swarm Intelligence Research (IJSIR) Email: ptsai@swin.edu.au Jianhui Lv . His research interests include machine learning, granular computing, rough sets and swarm intelligence. DOI: https://doi.org/10.11772/j.issn.1001-9081.2016.10.2777. 11, pp. Journal of Nanjing Normal University (Natural Science Edition), vol. An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. MATH 26, no. Wireless Personal Communications, vol. 7, pp. 219, Article number 106894, 2021. 28, no. 16011622, 2017. A reputation-based multi-user task selection incentive mechanism for crowdsensing. DOI: https://doi.org/10.1007/s40747-016-0011-y. 113, 2019. In Proceedings of IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Orlando, USA, pp. 3841, 2012. 446460, 2022. Counteracting estimation bias and social influence to improve the wisdom of crowds. M. R. Chen, Y. Y. Huang, G. Q. Zeng, K. D. Lu, L. Q. Yang. 134142, 1991. 26, no. 33, no. 39, no. 175, Article number 114812, 2021. Applied Soft Computing, vol. X. F. Liu, Z. H. Zhan, Y. Gao, J. Zhang, S. Kwong, J. Zhang. DOI: https://doi.org/10.1109/TEVC.2008.2009457. IEEE Transactions on Evolutionary Computation, vol. Science China Information Sciences, vol. Nonetheless, in the writers' community, we are known for our strict selection process. 22, pp. DOI: https://doi.org/10.1016/j.knosys.2021.106770. (in Chinese), B. Shen, W. Zhang, H. Y. Zhao, Z. Jin, Y. H. Wu. G. Wu, Z. Y. Chen, J. Liu, D. H. Han, B. Y. Qiao. 126, pp. 3, pp. IEEE Transactions on Cybernetics, to be published. Applied Soft Computing, vol. DOI: https://doi.org/10.1109/TETCI.2019.2961190. Development of differential evolution algorithm. DOI: https://doi.org/10.1109/TIE.2021.3091921. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. 5, pp. 14, pp. 6479, 2020. 11, pp. DOI: https://doi.org/10.1016/j.infsof.2013.12.001. Acta Electronica Sinica, vol. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. 1664516661, 2020. M. S. Lobo, D. Yao. IEEE Transactions on Services Computing, vol. Information Sciences, vol. 18, no. W. Du, L. Tong, T. Yang. 141, no. 8, pp. DOI: https://doi.org/10.1007/s00265-012-1423-3. Multimedia Tools and Applications, vol. Simultaneous instance and feature selection and weighting using evolutionary computation: Proposal and study. DOI: https://doi.org/10.1007/978-3-030-12127-3_5. DOI: https://doi.org/10.1109/ACCESS.2017.2731360. Y. X. Shen, G. Y. Wang, C. H. Zeng. (in Chinese). 1, pp. 24872497, 2017. - 88.99.137.80. 12731277, 2020. 1, pp. DOI: https://doi.org/10.1007/s10462-021-10042-y. 2, no. Particle swarm optimization for prototype reduction. AI with Swarm Intelligence. T. Q. Chang, D. P. Kong, N. Hao, K. H. Xu, G. Z. Yang. DOI: https://doi.org/10.1007/978-3-030-58115-2_4. 393401, 2017. Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey. DOI: https://doi.org/10.1016/j.patrec.2004.10.027. Intell. A new metaheuristic bat-inspired algorithm. 55, no. https://doi.org/10.1007/s11633-022-1367-7, https://doi.org/10.13328/j.cnki.jos.006313, https://doi.org/10.1007/s11633-022-1317-4, https://doi.org/10.1007/978-3-642-58069-7_38, https://doi.org/10.1016/j.swevo.2018.04.011, https://doi.org/10.1016/j.advengsoft.2013.12.007, https://doi.org/10.1016/j.biosystems.2017.07.010, https://doi.org/10.1007/978-3-030-12127-3_5, https://doi.org/10.1016/S0165-0114(03)00114-3, https://doi.org/10.1007/978-3-540-88051-6_4, https://doi.org/10.1007/978-3-642-30976-2_50, https://doi.org/10.4109/TEVC.2013.2281535, https://doi.org/10.3969/j.issn.1000-3932.2007.03.001, https://doi.org/10.3321/j.issn:0254-4164.2008.07.003, https://doi.org/10.1109/TEVC.2021.3131236, https://doi.org/10.1016/j.swevo.2018.06.010, https://doi.org/10.1007/s10462-017-9562-6, https://doi.org/10.1016/j.eswa.2020.113678, https://doi.org/10.1016/j.knosys.2019.01.006, https://doi.org/10.1109/TEVC.2013.2297160, https://doi.org/10.1016/j.ins.2011.09.001, https://doi.org/10.1109/TCYB.2013.2239988, https://doi.org/10.1109/TEVC.2008.2009457, https://doi.org/10.1109/TEVC.2013.2281528, https://doi.org/10.1109/TSMC.2018.2855155, https://doi.org/10.1109/TCYB.2019.2944873, https://doi.org/10.1109/TEVC.2019.2944180, https://doi.org/10.1007/978-3-642-12538-6_6, https://doi.org/10.1016/j.knosys.2011.07.001, https://doi.org/10.1108/IJICC-02-2014-0005, https://doi.org/10.1007/s10462-015-9441-y, https://doi.org/10.1016/j.eswa.2010.03.067, https://doi.org/10.1016/j.asoc.2018.11.048, https://doi.org/10.1109/TITS.2022.3150471, https://doi.org/10.1109/TITS.2020.3025796, https://doi.org/10.1016/j.ins.2014.09.053, https://doi.org/10.1016/j.ins.2014.08.039, https://doi.org/10.1109/TCYB.2019.2943928, https://doi.org/10.1109/TCYB.2020.3028070, https://doi.org/10.1109/TEVC.2018.2880894, https://doi.org/10.1016/j.swevo.2018.07.002, https://doi.org/10.1109/TEVC.2021.3065659, https://doi.org/10.1016/j.eswa.2018.03.015, https://doi.org/10.1016/j.eswa.2021.114812, https://doi.org/10.1016/j.asoc.2018.08.012, https://doi.org/10.1007/s13042-018-0888-4, https://doi.org/10.1016/j.energy.2021.120153, https://doi.org/10.1016/j.knosys.2017.12.031, https://doi.org/10.1016/j.amc.2014.02.005, https://doi.org/10.1007/s11432-018-9752-9, https://doi.org/10.1016/j.ast.2016.11.012, https://doi.org/10.1016/j.ast.2018.06.013, https://doi.org/10.1109/TNNLS.2015.2479117, https://doi.org/10.1109/TMAG.2018.2839663, https://doi.org/10.1016/j.neucom.2018.06.032, https://doi.org/10.1007/s10489-018-1258-3, https://doi.org/10.1016/j.apm.2017.10.001, https://doi.org/10.11897/SP.J.1016.2019.01289, https://doi.org/10.1007/s10462-021-10042-y, https://doi.org/10.1007/s00500-020-04759-1, https://doi.org/10.1016/j.patrec.2004.10.027, https://doi.org/10.1016/j.knosys.2021.106894, https://doi.org/10.1080/03052150903247736, https://doi.org/10.1007/s42979-021-00741-2, https://doi.org/10.1109/TEVC.2015.2458037, https://doi.org/10.1109/TCYB.2016.2554622, https://doi.org/10.1007/s40747-016-0011-y, https://doi.org/10.1016/j.ins.2019.10.066, https://doi.org/10.1109/TEVC.2021.3100056, https://doi.org/10.1109/TCYB.2018.2832640, https://doi.org/10.1109/TCYB.2019.2933499, https://doi.org/10.1109/TEVC.2018.2875430, https://doi.org/10.1109/TCYB.2020.2977956, https://doi.org/10.1007/3-540-58484-6_269, https://doi.org/10.1109/SMC42975.2020.9283410, https://doi.org/10.1109/TEVC.2018.2868770, https://doi.org/10.1109/TP-DS.2016.2597826, https://doi.org/10.1109/TCYB.2022.3153964, https://doi.org/10.1109/CIDUE.2014.7007861, https://doi.org/10.1007/s00265-012-1423-3, https://doi.org/10.1109/ROBOT.2005.1570636, https://doi.org/10.1163/15685373-12342163, https://doi.org/10.1007/978-3-030-36708-4_54, https://doi.org/10.1109/TETCI.2020.2992778, https://doi.org/10.1016/j.asoc.2020.106680, https://doi.org/10.1016/j.advengsoft.2016.01.008, https://doi.org/10.1016/j.asoc.2021.107854, https://doi.org/10.1007/s00366-019-00917-8, https://doi.org/10.1016/j.knosys.2021.107543, https://doi.org/10.1016/j.engappai.2019.01.001, https://doi.org/10.1007/s13748-021-00244-4, https://doi.org/10.1016/j.asoc.2020.106560, https://doi.org/10.1016/j.asoc.2021.107394, https://doi.org/10.1007/s00521-019-04483-4, https://doi.org/10.1016/j.knosys.2016.04.005, https://doi.org/10.1016/j.ins.2015.09.009, https://doi.org/10.11772/j.issn.1001-9081.2016.10.2777, https://doi.org/10.16804/j.cnki.issn1006-3242.2017.03.017, https://doi.org/10.1007/978-3-030-12334-5_1, https://doi.org/10.3969/j.issn.1000-1220.2017.09.038, https://doi.org/10.1109/ACCESS.2020.2989406, https://doi.org/10.1109/ACCESS.2017.2731360, https://doi.org/10.11772/j.issn.1001-9081.2017.07.2039, https://doi.org/10.3969/j.issn.2095-2163.2019.01.024, https://doi.org/10.1007/s11704-019-9119-8, https://doi.org/10.1007/s00778-018-0516-7, https://doi.org/10.1007/978-981-10-3482-4_11, https://doi.org/10.1016/j.dss.2020.113449, https://doi.org/10.1186/s41044-016-0012-2, https://doi.org/10.1007/s11063-014-9343-z, https://doi.org/10.1016/j.knosys.2021.106770, https://doi.org/10.1016/j.swevo.2020.100732, https://doi.org/10.1109/SERVICES48979.2020.00040, https://doi.org/10.1109/tkde.2019.2935443, https://doi.org/10.1016/j.knosys.2022.108382, https://doi.org/10.1016/j.asoc.2020.106798, https://doi.org/10.1016/j.ins.2019.09.065, https://doi.org/10.1109/TEVC.2019.2893447, https://doi.org/10.1007/978-3-030-58115-2_4, https://doi.org/10.1109/SSCI44817.2019.9002754, https://doi.org/10.1109/TCYB.2019.2937565, https://doi.org/10.1109/TCYB.2022.3158391, https://doi.org/10.1016/j.neucom.2020.12.065, https://doi.org/10.1109/TEVC.2019.2912204, https://doi.org/10.1016/j.ins.2021.11.031, https://doi.org/10.1016/j.ins.2021.12.096, https://doi.org/10.1109/TCYB.2021.3102642, https://doi.org/10.1109/TEVC.2021.3097339, https://doi.org/10.1016/j.neucom.2014.06.067, https://doi.org/10.1016/j.eswa.2017.07.025, https://doi.org/10.1016/j.neucom.2008.03.008, https://doi.org/10.1109/TCYB.2015.2487318, https://doi.org/10.1016/j.patcog.2010.10.020, https://doi.org/10.1016/j.asoc.2015.07.046, https://doi.org/10.1016/j.knosys.2017.07.005, https://doi.org/10.1109/TSMCB.2012.2188509, https://doi.org/10.1109/TETCI.2019.2961190, https://doi.org/10.1109/ICCA.2019.8899987, https://doi.org/10.11897/SP.J.1016.2021.01967, https://doi.org/10.1109/TAES.2018.2831138, https://doi.org/10.1016/j.ast.2018.05.039, https://doi.org/10.1016/j.asoc.2018.06.014, https://doi.org/10.1109/TCYB.2016.2535153, https://doi.org/10.1016/j.ast.2019.03.054, https://doi.org/10.1016/j.eswa.2019.112844, https://doi.org/10.3778/j.issn.1002-8331.2011-0416, https://doi.org/10.7544/issn1000-1239.2020.20190626, https://doi.org/10.1016/j.infsof.2013.12.001, https://doi.org/10.3969/j.issn.1001-4616.2019.02.001, https://doi.org/10.1142/S0218001420550113, https://doi.org/10.1007/s11042-019-07976-5, https://doi.org/10.1016/S0950-5849(01)00189-6, https://doi.org/10.1016/j.jss.2016.09.015, https://doi.org/10.1109/TPDS.2017.2735400, https://doi.org/10.1016/j.ins.2016.01.090, https://doi.org/10.1016/j.eswa.2007.04.017, https://doi.org/10.1007/s11277-016-3564-6, https://doi.org/10.1007/s41066-017-0048-3. 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