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冯艳红

作者:网站管理员 日期:2024-02-29

    冯艳红,女,197810出生硕士,教授,2006年毕业于华北电力大学,获得工学硕士学位,以第一作者发表SCI论文十余篇、中文核心期刊论文4篇、其他论文1篇,主持国家级课题1项、省部级课题1项、市厅级课题1项、校级课题2项。

     

    主要研究方向

    人工智能,计算智能,组合优化










    一、 主要科研成果(不超过10个)

    科研项目:

    1. 国家自然科学基金青年基金项目“基于学习机制的群智能算法及其应用研究”(NO.61806069),主持,结题,2019.1.1-2021.12.31.

    2. 河北省重点研发计划自筹项目“基于种群多样性的帝王蝶优化算法研究及在复杂组合优化问题中的应用”(NO.17210905),主持,结题,2017.12-2020.12.

    3. 河北地质大学国家预研项目“基于动态拓扑结构的群智能算法求解复杂约束背包问题研究”(KY2024YB06),主持,在研,2024.1-2025.12.

    4. 河北地质大学青年基金项目“基于新颖的群智能算法求解扩展背包问题的研究”(NO.QN201601),主持,结题,2016.1-2017.5.

     

    二、 代表性论文及著作(不超过10篇)

    1. 冯艳红, 王改革, 李明亮, 李晰. 基于自适应ε约束处理法的改进蛾子搜索算法. 模式识别与人工智能, 2023, 36(6): 483-494.

    2. Feng Y, Wang H, Cai Z, et al. Hybrid learning moth search algorithm for solving multidimensional knapsack problems. Mathematics, 2023, 11(8): 1811.

    3. Feng Y., Deb S, Wang G G, et al. Monarch butterfly optimization: a comprehensive review. Expert Systems with Applications, 2021, 168: 114418.

    4. Feng Y., Wang G G. A binary moth search algorithm based on self-learning for multidimensional knapsack problems. Future Generation Computer Systems, 2022, 126: 48-64.

    5. Feng, Y., Wang, GG., Li, W. et al. Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem. Neural Computing & Application 30, 30193036 (2018).

    6. Feng Y., Wang G G, Deb S, et al. Solving 0-1 knapsack problem by a novel binary monarch butterfly optimization. Neural Computing & Applications, 2017, 28(7):1619-1634.

    7. Feng Y., Yang J, Wu C, et al. Solving 0-1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation. Memetic Computing, 2016:1-16.

    8. Feng Y., Wang G G, Wang L. Solving randomized time-varying knapsack problems by a novel global firefly algorithm. Engineering with Computers, 2017(3):1-15.

    9. Feng, Y., Wang, G. G., Dong, J., & Wang, L. (2018). Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem. Computers & Electrical Engineering, 67, 454-468.

    10. Feng Y., Wang G G, Feng QJ. An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled FrogLeaping Algorithm for 0-1 Knapsack Problems. Computational Intelligence and Neuroscience, vol. 2014, Article ID 857254, 17 pages, 2014.

    四、联系方式

    Emailqinfyh@163.com

    电话13731150988

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