关于我们
范勤勤

个人简介

 1986年生,浙江宁波人,博士、副教授、硕士生导师(物流工程、控制科学与工程)博士生导师(管理科学与工程)长期从事进化计算、机器学习、过程控制与优化等方面研究总计发表各类学术论文40余篇;其中,在ieee t. cyberntics、ieee t. smcaejor、eswa、chemometr. intell. lab.等国际著名sci期刊上发表论文20余篇。主持国家自然科学基金青年项目。

办公地址:科研楼429b

电子邮箱:qqfan(at)shmtu.edu.cn; forever123fan(at)163.com

researchgate: https://www.researchgate.net/profile/qinqin_fan

学生:招收硕士研究生(控制理论与控制工程,物流工程),博士研究生(管理科学与工程)报考前务必邮件联系!


工作、学习经历:

§2019.09—:普渡大学,访问学者

§2018.07—:上海海事大学物流研究中心,副教授

§2018.04—:上海交通大学,博士后

§2015.09—2018.07:上海海事大学物流研究中心,讲师

§2012.09—2015.06:华东理工大学信息科学与工程学院,获博士学位

§2011.05—2012.08:浙江工程设计有限公司电仪室,助理工程师

§2008.09—2011.03:华东理工大学信息科学与工程学院,获硕士学位

§2003.09—2007.07:武汉工程大学电气信息学院,获学士学位

研究兴趣:

1. 约束优化、多目标优化、进化算法、机器学习、超启发式算法

2. 物流运作优化、博弈优化、动态优化等

3. 工业应用

主持科研项目:

6. 中国博士后科学基金面上资助, 2019-2020,项目号:2018m642017.

5. 上海海事大学顶级期刊论文培育基金,2018.04-2020.03.

4. 国家自然科学基金青年项目,2017.01—2019.12,项目号:61603244.

3. 化工过程先进控制和优化技术教育部重点实验室开放基金课题,2017.01—2018.12,项目号:2017acocp04.

2. 中美计算机科学研究中心开放基金课题,2016.04—2017.04,项目号:kjr1611.

1. 上海高校青年教师培养资助计划,2016.01—2017.12.

学术成果:

in press

[1] q.q. fan*, x.f. yan, y.l. zhang, c.m. zhu. a variable search space strategy based on sequential trust region determination technique. ieee transactions on cybernetics. doi:10.1109/tcyb.2019.2914060. 2019.(accepted)

[2] q.q. fan*, x.f. yan. solving multimodal multi-objective problems through zoning search. ieee transactions on systems, man, and cybernetics: systems. doi:10.1109/tsmc.2019.2944338. 2019. (accepted)

[3] y.l. zhang, y.f. zhu, q.q. fan. a novel set-membership estimation approach for preserving security in networked control systems under deception attacks. neurocomputing.  doi:https://doi.org/10.1016/j.neucom.2019.04.082.2019. (accepted)

[4] h. fan, q.q. fan*, w.l. wang. negatively correlated search based on multi-neighborhood generation strategy and its application in exit layout for crowd evacuation. world scientific research journal, 2019. (accepted)

[5] 毕超超,范勤勤*,王维莉. 基于策略自适应的多目标差分进化算法及其应用.计算机应用研究. 2019.(accepted)

[6] q.q. fan*, b. cao. a zoning search strategy for differential evolution variants. ieee ssci2019. (accepted)

[7] c. jiang, q.q. fan*. state parameter prediction of fire based on extreme learning machine. ieee ssci2019. (accepted)

2020

[34] 徐航,张依恋,朱瑾,范勤勤. 基于模型预测的自动导引车区间轨迹跟踪控制.控制理论与应用. 2020,37(1):23-30.

[33] 范勤勤*柳缔西子,王筱薇,韩新,王维莉.基于反向学习的微种群教与学优化算法及其应用.郑州大学学报(工学版),2020,41(1):19-27.微种群教与学优化算法求解博弈问题。

[32] q.q. fan*, y.l. zhang, z.h. wang. improved teaching learning based optimization and its application in parameter estimation of solar cell models. intelligent automation and soft computing, 2020. 26(1):1-11.(sci)改进教与学算法用于太阳能电池参数估计。

2019

[31] q.q. fan*, w.l. wang, x. han, b.h. cong. state parameters prediction of fire using generalized regression neural network. 2019 ieee international conference on smart manufacturing, industrial & logistics engineering (smile), hangzhou, china, 2019, pp. 150-154.(ei) 火灾状态参数预测,可被用于人群疏散和消防调度

[30] 黄敬英,范勤勤*.区块链在医联体中的应用浅析.医学信息学杂志. 2019. 40(10):30-34.

[29] 余佳,王维莉,韩新,范勤勤,胡志华. 考虑逆向物流的应急物资配置流程spn建模分析.中国安全生产科学技术. 2019.15(4):12-18.petri网络构建应急逆向物流。

[28] q. liao, q.q. fan*, j.j. li. translation control of an immersed tunnel element using a multi-objective differential evolution algorithm. computers & industrial engineering. 2019. 130:158-165. (sci, 三区)多目标差分进化算法在沉管浮运中的应用。

[27] q.q. fan*, w.l. wang, and x.f. yan. differential evolution algorithm with strategy adaptation and knowledge-based control parameters. artificial intelligence review, 2019. 51(2):219-253. (sci, 二区)de算法:利用学习遗忘机制来实现变异策略和交叉策略自适应,并使用先验知识和反向学习所得知识来引导de控制参数的进化

[26] q.q. fan,n. li, y.l. zhang, x.f. yan. zoning search using a hyper-heuristic algorithm. science china information sciences. 2019. 62: 000000:1-000000:3(sci,二区)基于性能驱动的分布式优化利用搜索空间的分割来降低问题的复杂性(分布式优化),并使用超启发式算法来实现算法自动选择(性能驱动)

[25] q.q. fan*, yaochu jin, weili wang, xuefeng yan. a performance-driven multi-algorithm selection strategy for energy consumption optimization of sea-rail intermodal transportation. swarm and evolutionary computation. 2019. 44: 1-17.(sci, 二区top)单目标超启发式算法:基于性能驱动来实现算法的自动选择(子算法数量≥3)。应用:海铁联运能耗优化。

2018

[24] q.q. fan and x.f. yan. multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective optimization of p-xylene oxidation reaction process. journal of intelligent manufacturing, 2018. 29(1): 35-49.(sci, 二区)多目标优化:利用存档机制来提升多目标差分进化算法性能应用:px氧化反应过程优化。

[23] q.q. fan*, x.f. yan, y.l. zhang. auto-selection mechanism of differential evolution algorithm variants and its application. european journal of operational research, 2018, 270(2): 636-653 (sci, 二区top)单目标超启发式算法:实现算法根据问题进行自动选择(子算法数量=2)

[22] q.q. fan*, y.l. zhang, x.f. yan, z.h. wang. enhancing the performance of jade using two-phase parameter control scheme and its application. international journal of automation and computing, 2018. 15(4):462-473.(ei)de算法:利用两种不同的参数控制方式来指引de参数进化。

[21] q. liao, q.q. fan*. applying a multi-objective differential evolution algorithm in translation control of an immersed tunnel element. //international conference in swarm intelligence. springer, cham, 2018: 243-250.(ei)

[20] z.h. wang, j.k. hu, q.q. fan. extracting the main routes and speed profiles between s from massive uncertain historical trajectories. //2018 international congerence on sensing and instrumentation in iot era (issi).  2018.(ei)大数据:航运轨迹提取

[19] 柳缔西子,范勤勤*,胡志华. 基于混沌搜索和权重学习的教与学算法优化及其应用.智能系统学报,2018, 13(5):818-828.

[18] 翟双爱,范勤勤*,胡志华. 基于混合知识的自适应粒子群算法在博弈问题中的应用.华东理工大学学报,2018, 44(4):595-608.

[17] 黄敬英,范勤勤*. 智能时代下的手术室护理管理浅谈.中华医院管理杂志. 2018, 34(2):153-156.

2017

[16] q.q. fan*, x.f. yan, and y. xue. prior knowledge guided differential evolution. soft computing, 2017. 21(22): 6841-6858.(sci, 三区)de算法:利用先验知识指引de控制参数进化

[15] q.q. fan*, w.l. wang, and x.f. yan. multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and biochemical dynamic optimization problems. applied soft computing, 2017, 59: 33-44.(sci, 二区)多目标优化:利用性能指标实现多目标差分进化算法变异策略自适应应用:化学和生物化学动态优化问题。

[14] j.j. li, b.w. xu, q.q. fan. immersed tunnel element translation control under current flow based on particle swarm optimization[c]//international conference in swarm  intelligence. springer, cham, 2017: 218-224.(ei)

2016

[13] q.q. fan and x.f. yan. self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. ieee transactions on cybernetics, 2016, 46(1): 219-232.(sci,一区top)de算法实现de控制参数的分区进化,生成多个控制参数高斯生成模型。

[12] q.q. fan* and y.l. zhang. self-adaptive differential evolution algorithm with crossover strategies adaptation and its applications in parameter estimation. chemometrics and intelligent laboratory systems, 2016, 151:164-171.(sci, 二区)de算法:实现de算法交叉策略自适应。应用:汞氧化反应动力学模型参数估计。

2015

[11] q.q. fan and x.f. yan. self-adaptive differential evolution algorithm with discrete mutation control parameters. expert systems with applications, 2015, 42(3):1551-1572. (sci, 二区)de算法:提出一种新的控制参数编码方式,使参数控制从种群→个体基因。

[10] q.q. fan and x.f. yan.differential evolution algorithm with self-adaptive strategy and control parameters for p-xylene oxidation process optimization. soft computing, 2015, 19(5): 1363-1391.(sci, 三区)de算法:实现de算法控制参数和变异策略自适应。应用:px氧化反应过程优化。

[9] q.q. fan,x.h. wang, and x.f. yan. harmony search algorithm with differential evolution based control parameters co-evolution and its application in chemical process dynamic optimization. journal of central south university, 2015, 22(6): 2227-2237.(sci)

[8]王应虎,范勤勤,颜学峰. 多样性分布参数的粒子群算法及其在过程动态优化中的应用. 化工自动化及仪表,2015,3:277-281.

2014

[7] q.q. fan and x.f. yan. self-adaptive particle swarm optimization with multiple velocity strategies and its application for p-xylene oxidation reaction process optimization. chemometrics and intelligent laboratory systems, 2014,12,139(15): 15-25.(sci, 二区)粒子群算法:实现pso算法控制参数自适应和使用多个速度更新策略。应用:px氧化反应过程优化。

[6] y.m. dong,q.q. fan, and x.f. yan. development of a hybrid model for sodium gluconate fermentation by aspergillus niger. journal of chemical technology and biotechnology, 2014,89(12): 1875-1882.(sci, 二区)

[5] q.q. fan and x.f. yan. a hybrid differential evolution algorithm integrated with particle swarm optimization and its application in parameter estimation. journal of donghua university, 2014,31(2): 197-200.(ei)

2013

[4] q.q. fan, z.m. lv, and x.f. yan. chemical process dynamic optimization based on a hybrid differential evolution algorithm integrated with alopex. journal of central south university, 2013, 20(4): 950-959. (sci)算法混合:alopex不仅用来寻找de最佳的控制参数,并辅助de生成新个体。应用:求解化工动态优化问题。

2012

[3] q.q. fan, and x.f. yan. differential evolution algorithm with co-evolution of control parameters and penalty factors for constrained optimization problems. pacific journal of  chemical engineering, 2012, 7(2): 227-235.(sci)约束优化:de算法控制参数与惩罚因子协同进化。

2010

[2] 范勤勤,颜学峰. 控制参数协进化的差分进化算法及其应用.石油化工高等学校学报,2010, 23(1):93-98.

[1] 范勤勤,颜学峰. 基于自适应差分进化算法的间歇反应动态优化求解.华东理工大学学报,2010, 36(6):832-838.

审稿人:

ieee transactions on evolutionary computation, ieee transactions on industrial electronics, ieee transactions on cybernetics, european journal of operational research, applied soft computing, swarm and evolutionary computation, soft computing, artificial intelligence review, 软件学报等期刊。


 

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