Personal Profile

赵宏，博士，2020年毕业于华南理工大学计算机科学与工程学院，师从IEEE Fellow詹志辉教授，专注于人工智能优化算法及其应用的研究。自2021年加入西安电子科技大学以来，致力于科学研究与成果转化，主持了8项科研项目，并作为技术负责人参与了4个项目，包括国家自然科学基金、广州市重点研发项目、广东省面上基金和广东省青年基金等。

在多AGV智能调度与优化领域进行了深入的实践研究，为复杂工厂环境下的优化问题提供了有效的解决方案，其设计的调度算法已成功实现商用落地。此外，在2021年新进教师中获得了西安电子科技大学“优秀学员”称号，在2022年华为发布的ADN自动驾驶网络难题中荣获“火花奖”。自2023年起，担任智能感知与计算师生联合党支部书记，并主讲本科生课程《人工智能导论》和研究生课程《算法设计与分析》。

截至目前，已指导硕士研究生25名，其中10人已毕业，3人获得校级优秀毕业生称号。所在团队近年来承担和参与了多项国家级重要科研项目，如国家“973”、“863”计划、国家科技支撑计划、国家发展与改革委员会示范工程以及国家自然科学基金重点项目等，发表了一系列高水平学术论文。这些成果已在顶级国际期刊和会议上发表，如IEEE Transactions on Evolutionary Computation、IEEE Transactions on Cybernetics、计算机学报及International Symposium on Neural Networks，累计发表高水平论文20余篇，授权国家发明专利12项，其中2项已成功转化。

积极参加国际学术交流活动，曾在IEEE Congress on Evolutionary Computation (CEC 2020)、Genetic and Evolutionary Computation Conference 2022 (GECCO’22) 和 GECCO’24上进行口头成果汇报。还受邀担任ICACI 2021程序委员会委员和中国计算机学会协同计算专委会委员，并被多个国际著名期刊邀请为审稿人。如

• IEEE Transactions on Evolutionary Computation (IF=11.7／2024, 中科院一区,计算智能领域顶级期刊)

• IEEE Transactions on Cybernetics (IF=9.4/2024, 中科院一区,计算智能领域顶级期刊)

 2021年1月入职以来，已经获批项目8项，作为技术负责人参与项目4项，其中国家自然科学基金1项，省级项目3项，地市级项目2项，横向项目6项，部分信息如下：

• 国家自然科学基金-青年项目    2024-2026年

• 广州市重点研发项目     2022-2025年

• 广东省自然科学基金-面上项目   2022-2025年

• 区域联合基金-青年基金项目   2021-2024年

• 广州市博士青年科技项目     2021-2024年 

• 招生信息：每年5个硕士招生名额

• 基本要求：要求学生热爱科研、勤奋刻苦、服从团队管理
  

• 欢迎对智能计算、智能调度、进化优化、复杂网络优化、和机器学习等研究方向感兴趣的同学加入我们！

• 可通过邮件与我联系：hongzhao@ xidian.edu.cn
  

已发表论文

[1] Hong Zhao (赵宏), Xu-Hui Ning, Jing Liu, “Evolutionary Multi-task Framework with Bi-Knowledge Transfer for Multimodal Optimization Problems ”, IEEE Transactions on Evolutionary Computation, 1-15, 2025.5. (中科院一区，IF/2024=11.7, 计算智能领域顶级期刊)

[2] Hong Zhao (赵宏), Zhi-Hui Zhan, et. al., “Local Binary Pattern Based Adaptive Differential Evolution for Multimodal Optimization Problems,” IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3343-3357, July 2020. (中科院一区, IF/2023=11.8, 在计算机-控制领域23本国际期刊中排名第一)

[3] HaoNang Huang, Hong Zhao* (赵宏), and Jing Liu, “MOMC3D：A novel multi-objective optimization method with mixed-coding strategy for standard cells in chip 3D placement”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2025.4. (CCF A 类期刊)

[4] 赵宏, 李珈瑞, 刘静, “基于局部时空的多峰优化算法及其在PID控制中的应用”, 计算机学报, vol. 47,  no. 6, pp. 1323-1340, 2024. (一级学会期刊)

[5] Hong Zhao (赵宏), Jing Liu, “Differential Evolution with Outlier-based Selection Approach for Multimodal Optimization Problems,” Applied Soft Computing, vol. 140, 110264, 2023. (中科院一区，IF/2023=8.7)

[6] Hong Zhao (赵宏), Ling Tang, Jing Liu, “Strengthen Evolution-based Differential Evolution with Prediction Strategy for Multimodal Optimization and Its Application in Multirobot Task Allocation”, Applied Soft Computing, vol. 139, 110218, 2023. (中科院一区，IF/2023=8.7)

[7] Hong Zhao(赵宏), XuHui Ning, XiaoTao Liu, Chao Wang, Jing Liu, “What makes Evolutionary Multi-Task Optimization better: A Comprehensive Survey”, Applied Soft Computing, vol. 110545, 156849, 2023. (中科院一区，IF/2023=8.7)

[8] XiangQian Li, Hong Zhao*(赵宏), Jing Liu, “Minimum Spanning Tree Niching-based Differential Evolution with Knowledge-Driven Update Strategy for Multimodal Optimization Problems”, Applied Soft Computing, vol. 145, 110589, 2023. (中科院一区，IF/2024=7.2)

[9] Tao Ma, Hong Zhao*(赵宏), Jing Liu, “Coarse- and Fine-grained Combined Clustering-based Differential Evolution for Multimodal Optimization Problems and Its Application in Multirobot Task Allocation”, Swarm and Evolutionary Computation, vol. 83, 101412, 2023. (中科院一区，IF/2024=8.2)

[10] Tao Ma, Hong Zhao*(赵宏), Xiangqian Li, Fang Yang, Chun-sheng Liu, Jing Liu, Reinforcement learning assisted differential evolution with adaptive resource allocation strategy for multimodal optimization problems, Swarm and Evolutionary Computation, Volume 94, 2025, 101888.

[11] Tao Ma, Li Guang Xie, Hong Zhao*(赵宏), Fang Yang, Chunsheng Liu, Jing Liu, A novel decision-making agent-based multi-objective automobile insurance pricing algorithm with insurers and customers satisfaction, Information Sciences, Volume 693, 2025, 121665.

[12] Tao Ma, Hong Zhao*(赵宏), Ling Tang, Mingsheng Xue,, and Jing Liu, Efficient Black-Box Attack with Surrogate Models and Multiple Universal Adversarial Perturbations, Scientific Reports, Accept, 2025.1.

[13] Xiyuan Chen, Hong Zhao*(赵宏), Jing Liu, “A Network Community-based Differential Evolution for Multimodal Optimization Problems”, Information Science. vol. 645, 119359, 2023. (IF/2023=8.1)

[14] Hong Zhao (赵宏), Zhi-Hui Zhan, et. al.,” Multiple Populations Co-evolutionary Particle Swarm Optimization for Multi-objective Cardinality Constrained Portfolio Optimization Problem,” Neurocomputing, vol. 430, pp. 58-70, 2021. (中科院二区，IF/2024=5.5

[15] Hong Zhao (赵宏), Jia Rui Li, and Jing Liu. “Localized Distance and Time-Based Differential Evolution for Multimodal Optimization Problems,” In Proceedings of the Genetic and Evolutionary Computation Conference 2022 (GECCO’22). ACM, New York. (IEEE旗舰国际会议，CCF C类)

[16] Jiang Zhu, Hong Zhao*, He Yu, and Jing Liu. 2024. Pixel Logo Attack: Embedding Attacks as Logo-Like Pixels. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. ACM .(IEEE旗舰国际会议, CCF C类)

[17] Wanqiu Zhao, Qi Qiu, Hong Zhao*(赵宏), et. al., 2024. Multi-population for Multi-objective Genetic Algorithm with Adaptive Information Sharing for Berth Allocation and Quay Crane Assignment Problems. In Proceedings of the Genetic and Evolutionary Computation Conference 2024 (GECCO ’24), July 14-18, 2024, ACM, Melbourne, VIC, Australia.(IEEE旗舰国际会议, CCF C类)

[18] Xuhui Ning, Hong Zhao*(赵宏), Xiaotao Liu, Jing Liu, “An Evolutionary Multi-Task Genetic Algorithm with Assisted-task for Flexible Job Shop Scheduling” Chinese CSCW 2022, accepted. （协同计算专委会议）

[19] Hong Zhao , Xiangqian Li, Jing Liu, “A Reachability-distance based Differential Evolution with Individual Transfer for Multimodal Optimization Problems,” in Proc. IEEE Congress on Evolutionary Computation, 1-8，2023.4. (IEEE旗舰国际会议)

[20] Hong Zhao, Zhi-Hui Zhan, et. al., “Adaptive Guidance-based Differential Evolution with Archive Strategy for Multimodal Optimization Problems,” in Proc. IEEE Congress on Evolutionary Computation, Glasgow, UK, pp. 1-8, Jul. 2020. (IEEE旗舰国际会议)

[21] Hong Zhao , Zhi-Hui Zhan, et. al., “An Improved Selection Operator for Multi-Objective Optimization,” in Proc. International Symposium on Neural Networks (ISNN 2019), Moscow, Russia, Jul. 2019, pp. 379-388. (神经网络旗舰国际会议)

[22] Shihao Yuan, Hong Zhao, Jing Liu, “Self-organizing Map Based Differential Evolution with Dynamic Selection Strategy for Multimodal Optimization Problems”, Mathematical Biosciences and Engineering, vol. 19, no. 6, pp. 5968-5997, 2022.

[23] Hong Zhao , Zhi-Hui Zhan, et. al., “A Multi-angle Hierarchical Differential Evolution Approach for Multimodal Optimization Problems”，IEEE Access, vol. 8, pp. 178322-178335, 2020.

已授权专利

    [1] 赵宏, 刘洋, 刘静, “基于智能预测和误差识别的多AGV规划方法及系统,” 专利号：202111644827.2, 已转让, 金额: 5万元，2022. (通过该专利的技术转化显著缩短了物料搬运的时间，加快了整个生产线的速度，提高整体生产效率)

[2] 赵宏, 袁锴薪, 刘静, “面向复杂环境的带实时冲突消解的多AGV智能协同调度方法,” 专利号：202110958806.1，已转让，金额：5万元, 2023. (通过该项技术的转化有效避免多AGV之间的碰撞和堵塞，保证各AGV在复杂环境下的顺畅运行，从而提高整个系统的作业效率)

[3] 赵宏, 袁锴薪, 刘静, “一种基于蚁群优化算法的多AGV动态路径规划方法”, 已授权, 2023111894420, 2023.12.

[4] 赵宏, 李相前, 刘静, “基于遗传算法的分布式全流程作业车间调度方法及终端”, 已授权, 202310880659X, 2023.12.

[5] 赵宏,李艺帆, 刘静等 “一种大规模软件定义网络性能预测方法” ,  已授权, 2023 1 0127510.4, 2023.10.

[6] 刘静, 李艺帆, 赵宏等 “一种基于密母神经架构搜索的软件定义网络性能预测方法”, 已授权, 2023 10127530.1, 2023.11.

[7] 赵宏, 唐凌, 刘静,  “一种基于双层优化的大规模集成电路布局优化方法”, 已授权, 202211282004.4, 2023.4.

[8] 赵宏, 陈文玮,刘静, “基于遗传算法的大规模集成电路布局优化方法”, 已授权, 202211367045.3, 2023.4.

[9] 赵宏, 薛明胜, 刘静, “基于多重普遍对抗扰动的黑盒对抗样本生成方法、装置”, 已授权, 2024106610334, 2024.5.

[10] 赵宏, 谢礼光, 刘静, “一种应用于汽车保险定价的多目标优化方法及终端”,已授权, 202311868143X, 2023.12.

[11] 刘静, 陈玺元, 赵宏, “ 一种基于滑动窗口和离散差分进化算法的3D布局优化方法”,  已授权, 202211632016.5, 2023.8.

[12] 赵宏,刘洋,刘静,袁锴薪，基于进化算法的电镀线行车调度方法、装置及存储介质，已授权，2021 1 0826240.7，2025.3.