姓名：曹子建

职称：教授

电话：18991348718

邮箱：caozijian@xatu.edu.cn

教育背景

工学硕士 (计算机应用), bevictor伟德官网, 中国, 2003

工学博士 (智能系统与模式识别), 西安理工大学, 中国, 2020

学术兼职

中国计算机学会会员

研究领域

智能优化、演化计算、深度强化学习、智能博弈、网络安全

研究概况

曹子建，博士，教授，博导，2003年4月毕业于西安工业学院计算机系，获工学硕士学位；2020年1月毕业于西安理工大学自动化学院，获模式识别与智能系统专业工学博士学位。

主持军委科技委国防基础加强项目1项、科技部外国专家项目1项、陕西省科技厅自然科学基础研究计划项目2项，完成陕西省教育厅专项科学研究计划项目2项、西安市科技局科技计划项目1项，参与国家自然科学基金项目3项、中央军委科学技术委员会国防创新特区创新与攻关项目2项、陕西省科技厅重点研发计划项目1项，负责电力大数据等横向科研项目10余项。 获得陕西省高等学校科学技术奖二等奖和三等奖等奖各1次。在国际和国内知名期刊发表科研论文40余篇，其中SCI/EI检索20篇，申请并授权软件著作权6项、发明专利10余项。

研究课题

[1]国防基础加强：***有无人混合编队智能博弈仿真技术（2024-2025）；

[2]陕西省科技厅自然科学基础研究计划项目：高维环境下问题特征驱动的自主演化计算方法研究（2024-2025）；

[3]科技部-国家外国专家项目：面向大规模云任务调度的演化计算方法研究（2023-2024）；

[4]国防基础科研计划基础研究与前沿技术：***多域协同网络攻防试验验证技术（2021-2023）；

[5]国防基础加强,***无人机集群对抗策略推演的多智能体（2021-2024）；

[6]陕西省科技厅自然科学基础研究计划项目：演化数据特征驱动的大规模全局优化方法及其应用研究（2020-2021）；

[7]陕西省教育厅专项科学研究计划：基于智能优化的复杂网络社团检测方法研究（2017-2019）；

[8]陕西省教育厅专项科学研究计划：云计算环境下有监督聚类的入侵检测模型研究（2014-2015）；

[9]西安市科技局科技计划项目：基于大数据挖掘的锅炉高温金属材料寿命管理数据分析平台（2014-2015）。

奖励与荣誉

2020被评为优秀共产党员；

2018、2020、2022年度获得毕业设计（论文) 优秀指导教师。

学术成果

[1] Cao Z J, Xu K, Jia H W, Fu Y F, Foh C H, Tian F. An autonomous differential evolution based on reinforcement learning for cooperative countermeasures of unmanned aerial vehicles, Applied Soft Computing, 2025: 112605.

[2] Cao Z J,Xu K,Wang Z Y, Tian F. An adaptive population size based differential evolution by mining historical population similarity for path planning of unmanned aerial vehicles, Information Sciences, 2024, 666: 120432.

[3] Cao Z J, Jia H W, Wang Z Y, Chuan Heng Foh, Tian F. A differential evolution with autonomous strategy selection and its application in remote sensing image denoising, Expert Systems with Applications, 2024, 238: 122108.

[4] Li J, Cao Z J, Liu F G, Fu Y F, Li X, Tian F. An adaptive biogeography-based optimization with integrated covariance matrix learning for robust visual object tracking, Expert Systems with Applications, 2023, 234: 121110.

[5] Cao Z J, Li J, Fu Y F, Wang Z Y, Jia H W, Tian F. An adaptive biogeography-based optimization with cumulative covariance matrix for rule-based network intrusion detection, Swarm and Evolutionary Computation, 2022, 75: 101199.

[6] Cao Z J, Wang Z Y, Fu Y F, Jia H W, Tian F. An adaptive differential evolution framework based on population feature information, Information Sciences, 2022, 608: 1416-1440.

[7] Wang Z Y, Cao Z J, Haowen Jia. An adaptive moth flame optimization algorithm with historical flame archive strategy and its applications. Soft Computing, 2023, 27(17): 12155-12180.

[8] Wang Z Y, Cao Z J, Du Z Q, Jia H W, Han B H, Tian F, Liu F X. Differential evolution with autonomous selection of mutation strategies and control parameters and its application, Complexity, 2022(1): 7275088.

[9] Wang Z Y, Cao Z J, Liu C, et al. An Enhanced Moth-Flame Optimization with Multiple Flame Guidance Mechanism for Parameter Extraction of Photovoltaic Models, Mathematical Problems in Engineering, 2022.6.11.

[10] Cao Z J, Wang Z Y, Fu Y F, et al. An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation, Mathematical Problems in Engineering, 2022.2.27.

[11] Cao Z J, Liu C, Wang Z Y, et al. An Improved MOEA/D Framework with Mult-operator Strategies for Multi-objective Optimization Problems with a Large Scale of Variables, IEEE Congress on Evolutionary Computation. IEEE, 2021: 2164-2170.

[12] Jia H W,Cao Z J, Wang Z Y. An adaptive exploratory Q-learning algorithm for multiple target path planning, 17th International Conference on Computational Intelligence and Security (CIS), 2021:35-39.

[13] Cao Z J. Evolutionary optimization of artificial neural network using an interactive phase-based optimization algorithm for chaotic time series prediction [J]. Soft Computing, 2020, 24(22):17093-17109.

[14] Cao Z J, Wang L. An active learning brain storm optimization algorithm with a dynamically changing cluster cycle for global optimization [J]. Cluster Computing, 2019, 22(4):1413-1429.

[15] Cao Z J, Wang L. A comprehensive study of phase based optimization algorithm on global optimization problems and its applications [J]. Applied Intelligence, 2018, 49(4): 1355-1375.

[16] Cao Z J, Wang L, Hei X H. A global-best guided phase based optimization algorithm for scalable optimization problems and its application [J]. Journal of Computational Science, 2018, 25: 38-49.

[17] Cao Z J, Wang L. An Optimization Algorithm Inspired by the Phase Transition Phenomenon for Global Optimization Problems with Continuous Variables [J], Algorithms, 2017, 10(4): 1-21. (EI: 20175204573004)

[18] Cao Z J, Rong X F, Du Z Q. An Improved Brain Storm Optimization with Dynamic Clustering Strategy, ICMME[C]//2017: 19002.

[19] Cao Z J, Wang L, Hei X H, et al. A phase based optimization algorithm for big optimization problems [C] // Evolutionary Computation. IEEE, 2016: 5209-5214. (EI: 20165203176304)

[20] Cao Z J, Wang L, Shi Y H, et al. An effective cooperative co-evolution framework integrating global and local search for large scale optimization problems [C]//Evolutionary Computation. IEEE, 2015: 1986-1993.

[21] Cao Z J, Hei X H, Wang L, Shi Y H, Rong X F. An improved brain storm optimization with differential evolution strategy for applications of ANNs [J], Mathematical Problems in Engineering, 2015(2015): 1-18.

[22] Cao Z J, Shi Y H, Rong X F, Liu B L, Du Z Q, Yang B. Random grouping brain storm optimization algorithm with a new dynamically changing step size[C]// International Conference in Swarm Intelligence. Springer, 2015: 357-364.