姓名：杨舒玲

性别：女

学位/职称：工学博士/讲师

个人简介：

杨舒玲，博士毕业于华南理工大学，现任江西理工大学信息工程学院专任教师，主要从事人工智能、最优化、智能计算、进化优化等方面的研究。参与国家自然科学基金与省自然科学基金多项，积累大量研究基础与经验。在人工智能、最优化、智能计算、进化优化等领域发表高水平国际学术论文十余篇。指导学生获得国家级和省级赛事奖项十余次。欢迎志于科研的学生加入团队深造学习、共同成长。

学科专业：人工智能、计算机技术、计算机科学与技术、软件工程

本科生授课课程：《软件工程》、《JAVA EE程序设计》、《微信小程序开发设计》、《物联网技术与应用》

研究方向：智能计算、进化算法、进化深度学习及智能优化理论与应用研究

主持承担科研项目及经费：

[1] 江西理工大学博士科研启动基金项目，10万，主持

[2] 广东省基础与应用基础研究基金联合基金（青年），10万，主持

[3] 东莞理工学院人才科研启动基金项目，30万，主持

科研成果（获奖、专利等）：

获奖：

指导学生获得国家级人工智能相关赛事奖项五次，指导学生获省级人工智能相关学科竞赛奖项七次，荣获国家级与省级优秀指导教师十余次；获校级“优秀班主任”与教学竞赛三等奖等。

出版著作及代表性论文：

[1] Local-Diversity Evaluation Assignment Strategy for Decomposition-Based Multiobjective Evolutionary Algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(3): 1697-1709, 2023.（中科院SCI一区Top，IF：8.7）

[2] An Embedded Hamiltonian Graph-Guided Heuristic Algorithm for Two-Echelon Vehicle Routing Problem[J]. IEEE Transactions on Cybernetics, 52(7): 5695-5707, 2022. （中科院SCI一区Top，IF：10.5）

[3] A Dynamic Regularization-based Evolutionary Learning Algorithm For Many-objective Structural Equation Models [J]. Expert Systems with Applications,  Manuscript ID: ESWA-D-25-28520.（中科院一区Top，IF: 7.5）

[4] AI Empowered Intelligent Search for Path Planning in UAV-Assisted Data Collection Networks[J]. IEEE Internet of Things Journal, 11(21), pp. 34492-34503, 2024. （中科院SCI一区，IF：8.9）

[5] A Dynamic Regularization-based Evolutionary Learning Algorithm For Many-objective Structural Equation Models [J]. Expert Systems with Applications,  2026, Early Access.（中科院一区Top，IF：7.5）

[6] A Decision-Making Subgraph Mining Algorithm for Structural Equation Modeling[C]. 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), Hainan, China, 1286-1293: 2023.（CCF推荐会议）

[7]A deep reinforcement learning-guided multimodal multi-objective evolutionary algorithm with a serial-parallel mechanism[J]. Expert Systems with Applications, 2026, 298: 129581.（中科院一区Top，IF：7.5）

[8]A method for predicting the capacity of lithium-ion batteries based on Pearson correlation coefficient-guided multi-objective particle swarm optimization[J]. Computers & Industrial Engineering, 210: 111514, 2025. （JCR一区期刊，IF：6.5）

[9]  UAV-Assisted Data Collection and Transmission Using Petal Algorithm in Wireless Sensor Networks[C]. International Conference on Algorithms and Architectures for Parallel Processing, Springer Nature Singapore, 14496, 114-125: 2024.（CCF推荐国际学术会议）

[10] Improved LSTM Algorithm for WBGT Index Prediction in Smart Cities[C]. 2023 19th International Conference on Mobility, Sensing and Networking (MSN). IEEE Computer Society, 693-698: 2023.

[11] Dynamic Fitness Landscape Analysis on Differential Evolution Algorithm[C]. Communications in Computer and Information Science, 682: 179-184, 2017.

[12] Running-Time Analysis of Particle Swarm Optimization with a Single Particle Based on Average Gain[C]. Simulated Evolution and Learning: 11th International Conference, 515-527: 2017.

[13] Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape[J]. International Journal of Cognitive Informatics and Natural Intelligence, 13(1): 36-61, 2019.