王改革

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王改革

职称: 教授 学院: 计算机科学与技术学院 电子邮箱: wgg@ouc.edu.cn 办公室:
  • 基本信息
  • 项目
  • 获奖
  • 论文
  • 课程
  • 教材或专著
  • 专利
  • Research Fields
  • Achievement
  • Social Appointments
  • Teaching Experience
  • Honor Title
  • Additional Information
  • 基本信息
    姓名: 王改革 出生年月: 最高学位:博士 入职海大时间: 2017.11
    主要研究方向:群体智能及智能制造 专业技术职务:教授 导师类别:博导
    年均课时数: 是否第一学科:是
    国内外重要学术组织任职:1. 山东省人工智能学会 计算智能专委会 常务委员;2. 中国计算机学会数字农业分会 委员;3. 山东省人工智能学会常务理事; 4. 中国计算机学会青年计算机科技论坛 青岛分论坛 AC、副主席(2019年5月1日-2020年4月30日); 5. 中国仿真学会 智能仿真优化与调度专委会 委员、副秘书长;6. 江苏省自动化学会 智能优化专委会委员 委员;
    获得人才称号名称及获得年份:
    其他情况简介: 主要致力于群体智能及智能制造问题等方面研究。主持国家自然科学基金等科研项目4项,获得省部级奖4项。入选2022、2021年科睿唯安“全球高被引学者”和爱思唯尔2020-2022“中国高被引学者”榜单;入选MDPI 2021最具影响力作者奖;入选全球前2%顶尖科学家榜单。1篇论文入选2019年“中国百篇最具影响国际学术论文”;位列2019年度Springer Nature中国学者高影响力论文-计算机科学领域榜首;1篇论文入选科技部“精品期刊顶尖论文平台--领跑者5000”数据平台。担任IJAISC主编,Intelligent Marine Technology and Systems、Engineering Applications of Artificial Intelligence、Journal of Computational Design and Engineering、IEEE/CAA Journal of Automatica Sinica、Mathematics和IJBIC等SCI期刊的副主编或编委。
    2022.12—至今:中国海洋大学 信息科学与工程学部 计算机科学与技术学院 教授2017.11—2022.12:中国海洋大学 信息科学与工程学院 计算机科学与技术系 副教授2013.07—2017.11:江苏师范大学 智慧教育学院(计算机科学与技术学院) 讲师
  • 项目
  • 获奖
    • 1. 2022, 吴文俊人工智能科学技术奖, 三等奖, 2/5
    • 2. 2021, 北京市科学技术奖, 二等奖, 3/5
    • 3. 2018, 江苏省科学技术奖, 二等奖, 2/10
    • 4. 2017, 高等学校科学研究优秀成果奖, 二等奖, 5/8
  • 论文
    • 1. Solving Multi-Objective Fuzzy Job-shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm, IEEE Transactions on Industrial Informatics, 2022, SCI 1区 Top,
    • 2. Improving metaheuristic algorithms with information feedback models, IEEE Transactions on Cybernetics, 2019, SCI 1区 Top,
    • 3. Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism, IEEE Transactions on Fuzzy Systems, 2020, SCI 1区 Top,
    • 4. Interval multi-objective optimization with memetic algorithms, IEEE Transactions on Cybernetics, 2020, SCI 1区 TOP,
    • 5. Detection of malicious code variants based on deep learning, IEEE Transactions on Industrial Informatics, 2018, SCI 1区 TOP,
    • 6. Forecasting ENSO Using Convolutional LSTM Network with Improved Attention Mechanism and Models Recombined by Genetic Algorithm in CMIP5/6, Information Sciences, 2023, SCI 1区,
    • 7. Improved differential evolution using two-stage mutation strategy for multimodal multi-objective optimization, Swarm and Evolutionary Computation, 2023, SCI 1区,
    • 8. ConvUNeXt: An efficient convolution neural network for medical image segmentation, Knowledge-Based Systems, 2022, SCI 1 区,
    • 9. Multi-instance semantic similarity transferring for knowledge distillation, Knowledge-Based Systems, 2022, SCI 1区,
    • 10. A Review of Green Shop Scheduling Problem, Information Sciences, 2022, SCI 1区,
    • 11. A Survey of learning-based intelligent optimization algorithms, Archives of Computational Methods in Engineering, 2021, SCI 1区 Top,
    • 12. DLEA: a dynamic learning evolution algorithm for many-objective optimization, Information Sciences, 2021, SCI 1区,
    • 13. Monarch butterfly optimization: a comprehensive review, Expert Systems with Applications, 2021, SCI 1区,
    • 14. Novel binary-addition tree algorithm for reliability evaluation of acyclic multistate information networks, Reliability Engineering & System Safety, 2021, SCI 1区,
    • 15. Learning-based elephant herding optimization algorithm for solving numerical optimization problems, Knowledge-Based Systems, 2020, SCI 1区 Top,
    • 16. Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Information Sciences, 2020, SCI 1区 Top,
    • 17. Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Future Generation Computer Systems, 2020, SCI 1区,
  • 课程
  • 教材或专著
  • 专利
  • 成果转化与社会服务
  • Research Fields
  • Achievement
  • Social Appointments
    • 1. 2022, 吴文俊人工智能科学技术奖, 三等奖, 2/5
    • 2. 2021, 北京市科学技术奖, 二等奖, 3/5
    • 3. 2018, 江苏省科学技术奖, 二等奖, 2/10
    • 4. 2017, 高等学校科学研究优秀成果奖, 二等奖, 5/8
  • Teaching Experience
    • 1. Solving Multi-Objective Fuzzy Job-shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm, IEEE Transactions on Industrial Informatics, 2022, SCI 1区 Top,
    • 2. Improving metaheuristic algorithms with information feedback models, IEEE Transactions on Cybernetics, 2019, SCI 1区 Top,
    • 3. Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism, IEEE Transactions on Fuzzy Systems, 2020, SCI 1区 Top,
    • 4. Interval multi-objective optimization with memetic algorithms, IEEE Transactions on Cybernetics, 2020, SCI 1区 TOP,
    • 5. Detection of malicious code variants based on deep learning, IEEE Transactions on Industrial Informatics, 2018, SCI 1区 TOP,
    • 6. Forecasting ENSO Using Convolutional LSTM Network with Improved Attention Mechanism and Models Recombined by Genetic Algorithm in CMIP5/6, Information Sciences, 2023, SCI 1区,
    • 7. Improved differential evolution using two-stage mutation strategy for multimodal multi-objective optimization, Swarm and Evolutionary Computation, 2023, SCI 1区,
    • 8. ConvUNeXt: An efficient convolution neural network for medical image segmentation, Knowledge-Based Systems, 2022, SCI 1 区,
    • 9. Multi-instance semantic similarity transferring for knowledge distillation, Knowledge-Based Systems, 2022, SCI 1区,
    • 10. A Review of Green Shop Scheduling Problem, Information Sciences, 2022, SCI 1区,
    • 11. A Survey of learning-based intelligent optimization algorithms, Archives of Computational Methods in Engineering, 2021, SCI 1区 Top,
    • 12. DLEA: a dynamic learning evolution algorithm for many-objective optimization, Information Sciences, 2021, SCI 1区,
    • 13. Monarch butterfly optimization: a comprehensive review, Expert Systems with Applications, 2021, SCI 1区,
    • 14. Novel binary-addition tree algorithm for reliability evaluation of acyclic multistate information networks, Reliability Engineering & System Safety, 2021, SCI 1区,
    • 15. Learning-based elephant herding optimization algorithm for solving numerical optimization problems, Knowledge-Based Systems, 2020, SCI 1区 Top,
    • 16. Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Information Sciences, 2020, SCI 1区 Top,
    • 17. Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Future Generation Computer Systems, 2020, SCI 1区,
  • Honor Title
  • Additional Information