师资队伍

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206CB

赵春亮

职称: 讲师
学院: 数据科学学院
电子邮箱: zhaoclyx@gmail.com;zhaochunliang@qust.edu.cn
  • 基本信息

  • 项目

  • 获奖

  • 论文

  • 专利

  • 课程

  • 教材或专著

  • 基本信息
    姓名:  赵春亮                           最高学位:博士                                                入职科大时间:2022.07          
    主要研究方向:图神经网络、智能优化与学习、柔性调度、智慧交通、电路设计                    导师类别:硕士生导师          
    国内外重要学术组织任职:中国人工智能学会会员、中国自动化学会会员、山东宇航学会理事、山东省人工智能学会智能优化与调度专委会委员、山东省人工智能学会会员;Part C、Part E、Information sciences 、Applied Soft Computing、Knowledge-Based Systems、控制与决策等学术期刊审稿人
    学习研究经历:
    2018/08-2022/06:中山大学,计算机科学与技术,博士
    2015/09-2018/07:青岛科技大学,数学,硕士
    2011/09-2015/07:济南大学,信息与计算科学,学士
  • 项目
    [1]省级,可变场景下学习与演化共融的智能优化理论与应用(ZR2023QF065),青年自然基金,项目负责人。
  • 获奖
  • 论文
    [1] Zhao C, Zhou Y, Chen Z. Decomposition-based evolutionary algorithm with automatic estimation to handle many-objective optimization problem[J]. Information Sciences, 2021, 546: 1030-1046. (SCI一区TOP, IF 8.1)
    [2]JZhao C, Zhou Y, Lai X. An integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems[J]. Information Sciences, 2022, 600: 342-361. (SCI一区TOP, IF 8.1)
    [3] Zhao C, Zhou Y, Hao Y. Decomposition-based evolutionary algorithm with dual adjustments for many-objective optimization problems[J]. Swarm and Evolutionary Computation, 2022, 75: 101168. (SCI一区TOP, IF 10.0)
    [4] Hao Y, Zhao C*, Li Z, et al. A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization[J]. Knowledge-Based Systems, 2024, 286: 111366. (SCI一区TOP, IF 8.8)
    [5] Zhao C, Hao Y, Gong D, et al. An ensemble learning-based multi-population evolutionary framework for multi-scenario multi-objective optimization problems[J]. Knowledge-Based Systems, 2023: 110708. (SCI一区TOP, IF 8.8)
    [6] Zhao C, Zhou Y, Hao Y, et al. A bi-layer decomposition algorithm for many-objective optimization problems[J]. Applied Intelligence, 2022, 52(13): 15122-15142. (SCI二区, IF 5.3)
    [7] Hao Y, Si B, Zhao C. Topology transformation-based multi-path algorithm for urban rail transit network[J]. Transportation Research Part C: Emerging Technologies, 2022, 136: 103540. (SCI一区TOP, IF 9.02)
    [8] Hao Y, Si B, Zhao C. A novel shortest path algorithm with topology transformation for urban rail transit network[J]. Computers & Industrial Engineering, 2022, 169: 108223. (SCI二区, IF 7.9)
    [9] Hao Y, Bingfeng S, Zhao C. Streamlined Hierarchical Topology Network-Based Passenger Flow Assignment of Urban Rail Transit[J]. Transportation Research Record, 2022, 2676(8): 683-696. (SCI四区, IF 1.7)
  • 专利
    [1]授权国家发明专利: 一种基于拓扑变换的城市轨道交通分层网络的构建方法,CN114937364A,2022.08.23
  • 课程
    [1] 本科生课程,最优化方法,32学时
    [2] 本科生课程,信息检索,48学时
  • 教材或专著