个人简介

杨奇特，广东工业大学校聘副教授(合同约定)，2025年12月作为“青年百人计划”条件A层次人才加入计算机学院。博士毕业于华南理工大学，长期从事进化计算相关的优化算法设计及其应用研究，累计在IEEE TEVC、IEEE TCYB、IEEE TBD等国际权威学术期刊和会议上发表论文十余篇。担任IEEE TEVC、IEEE TCYB、IEEE TSMCA、IEEE TETCI、IEEE TAI等期刊审稿人。

工作邮箱：qiteyang@foxmail.com

期刊论文：

[1]. Qi-Te Yang, X. X. Xu, Z. H. Zhan, J. Zhong, S. Kwong, and J. Zhang, “Evolutionary multitask optimization for multiform feature selection in classification,” IEEE Transactions on Cybernetics, vol. 55, no. 4, pp. 1673-1686, 2025.

[2]. L. Y. Luo, Qi-Te Yang^*, S. Kwong, J. Zhang, and Z. H. Zhan, “A hybrid feature-based heterogeneous graph transformer method for cheating official account mining,” Complex & Intelligent Systems, 2025.

[3]. Qi-Te Yang, J. Y. Li, Z. H. Zhan, Y. Jiang, Y. Jin, and J. Zhang, “A hierarchical and ensemble surrogate-assisted evolutionary algorithm with model reduction for expensive many-objective optimization,” IEEE Transactions on Evolutionary Computation, 2024.

[4]. Qi-Te Yang, Z. H. Zhan, X. F. Liu, J. Y. Li, and J. Zhang, “Grid classification-based surrogate-assisted particle swarm optimization for expensive multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 28, no. 6, pp. 1867-1881, 2024.

[5]. Qi-Te Yang, Z. H. Zhan, S. Kwong, and J. Zhang, “Multiple populations for multiple objectives framework with bias sorting for many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 5, pp. 1340-1354, 2023.

[6]. J. Q. Yang, Qi-Te Yang (co-first author), K. J. Du, C. H. Chen, H. Wang, S. W. Jeon, J. Zhang, and Z. H. Zhan, “Bi-directional feature fixation-based particle swarm optimization for large-scale feature selection,” IEEE Transactions on Big Data, vol. 9, no. 3, pp. 1004-1017, 2023.

[7]. Z. Wang, Q. Li, Qite Yang, and H. Ishibuchi, “The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts” Complex & Intelligent Systems, vol. 9, no. 2, pp. 1117-1126, 2023.

[8]. Qite Yang, Z. Wang, J. Luo, and Q. He, “Balancing performance between the decision space and the objective space in multimodal multiobjective optimization,” Memetic Computing, vol. 13, no. 1, pp. 31-47, 2021.

[9]. J. Zou, Y. He, J. Zheng, D. Gong, Qite Yang, L. Fu, and T. Pei, “Hierarchical preference algorithm based on decomposition multiobjective optimization”, Swarm and Evolutionary Computation, vol. 60, 2021.

[10]. J. Zou, Qite Yang^*, S. Yang, and J. Zheng, “Ra-dominance: A new dominance relationship for preference-based evolutionary multi-objective optimization,” Applied Soft Computing (JCR Q1, IF = 6.6), vol. 90, 2020, Art. no. 106192.

会议论文：

[1]. Z. Z. Lu, Qi-Te Yang^*, K. J. Du, J. Y. Li, C. H. Chen, Q. Zhou, and Z. H. Zhan, “Bi-velocity coevolutionary multiswarm particle swarm optimization for many-objective gateway placement optimization,” in Proceedings of IEEE Congress on Evolutionary Computation, 2025.

[2]. Qi-Te Yang, L. Y. Luo, C. H. Chen, J. Y. Li, J. H. Zhong, J. Zhang, and Z. H. Zhan, “Surrogate-assisted flip for evolutionary high-dimensional multiobjective feature selection,” in Proceedings of IEEE Congress on Evolutionary Computation, 2024, pp. 1-8.

[3]. X. Y. Wang, Qi-Te Yang (co-first author), Y. Jiang, K. C. Tan, J. Zhang, and Z. H. Zhan, “Fine-grain knowledge transfer-based multitask particle swarm optimization with dual clustering-based task generation for high-dimensional feature selection,” in Proceedings of ACM Genetic and Evolutionary Computation Conference, 2024, pp. 1506-1514.

[4]. Qi-Te Yang, L. Y. Luo, X. X. Xu, C. H. Chen, H. Wang, J. Zhang, and Z. H. Zhan, “Conjugate surrogate for expensive multiobjective optimization,” in Proceedings of IEEE Symposium Series on Computational Intelligence, 2023, pp. 920-925.

[5]. Qi-Te Yang, Z. H. Zhan, Y. Li, and J. Zhang, “Social learning particle swarm optimization with two-surrogate collaboration for offline data-driven multiobjective optimization,” in Proceedings of ACM Genetic and Evolutionary Computation Conference, 2022, pp. 49-57.

[6]. Qite Yang, Z. Wang, and H. Ishibuchi, “It is hard to distinguish between dominance resistant solutions and extremely convex Pareto optimal solutions,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, 2021, pp. 3-14.

[7]. Z. Wang, J. Deng, Q. Zhang, and Qite Yang, “On the parameter setting of the penalty-based boundary intersection method in MOEA/D,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, 2021, pp. 413-423.

荣誉奖励

博士研究生国家奖学金，2024年