1990年9月至1995年7月 在清华大学自动化系过程控制专业学习，获学士学位
1995年9月至1999年10月 在清华大学自动化系控制理论与控制工程专业学习，获博士学位
1999年10月至2002年11月 清华大学自动化系过程控制工程研究所 讲师
2002年12月至2008年11月 清华大学自动化系过程控制工程研究所 副教授
2007年1月至2008年1月 美国密西根大学工业与运作工程系 访问学者
2008年12月至今 清华大学自动化系过程控制工程研究所 教授 博士生导师
[1] 中国仿真学会理事[2] 中国仿真学会智能优化与调度专委会副主任委员[3] 中国人工智能学会自然计算与数字智能城市专委会副主任委员[4] 中国自动化学会控制理论与应用专委会委员[5] 中国自动化学会过程控制专委会委员[6] 中国自动化学会能源互联网专委会常务理事[7] 中国运筹学会排序专委会常务理事[8] 中国运筹学会智能工业数据解析与优化专委会常务理事[9] 中国人工智能学会智能优化专委会常务理事[10] 北京自动化学会常务理事[11] International Journal of Automation and Control主编[12] Complex System Modeling and Simulation执行主编[13] IEEE Transactions on Evolutionary Computation副编辑[14] Swarm and Evolutionary Computation副编辑[15] Expert Systems with Applications副编辑[16] Sensors副编辑[17] Memetic Computing编委[18] Int J of Applied and Computational Mathematics副编辑[19] Int J of Artificial Intelligence and Soft Computing编委[20] Journal of Optimization编委[21] CAIE、JIM、CAIS、JSEE、NEUROCOMPUTING特邀编辑[22] 《控制理论与应用》编委[23] 《控制与决策》编委[24] 《控制工程》编委[25] 《系统工程与电子技术》编委
智能优化理论、方法与应用
复杂生产过程建模、优化与调度
[1]国家杰出青年科学基金(61525304)：智能优化调度理论与方法。(负责人) (2016.1~2020.12)
[2]国家自然科学基金项目(61873328)：分布式生产调度的协同群智能优化理论与方法。(负责人) (2019.1~2022.12)
[3]国家自然科学基金项目(61174189)：复杂资源受限项目调度问题及其混合智能算法研究。(负责人) (2012.1~2015.12)
[4]国家自然科学基金项目(70871065)：基于学习机制的群智能调度理论与方法研究。(负责人) (2009.1~2011.12)
[5]国家自然科学基金项目(60774082)：复杂生产系统基于差分进化和量子进化的优化调度理论与方法。(负责人) (2008.1~2010.12)
[6]国家自然科学基金项目(60374060)：复杂生产系统的智能仿真优化理论与方法研究。(负责人) (2004.1~2006.12)
[7]国家自然科学基金项目(60204008)：复杂系统基于计算智能的混合优化理论与方法。(负责人) (2003.1~2005.12)
[8]国家自然科学基金重点项目(60834004)：复杂芯片制造过程实时调度与优化控制理论和算法研究及应用。(骨干) (2009.1~2012.12)
[9]教育部新世纪优秀人才支持计划(NCET-10-0505)。(负责人) (2010.1~2012.12)
[10]高等学校博士学科点专项科研基金(20130002110057)：基于协同分布估计算法的分布式车间调度研究。(负责人) (2014.1~2016.12)
[11]高等学校博士学科点专项科研基金(20100002110014)：基于新型混合群智能的资源约束项目调度研究。(负责人) (2011.1~2013.12)
[12]北京市科技新星计划(2004A41)：混合智能优化调度理论与算法研究。(负责人) (2004.7~2007.7)
[13]教育部留学回国启动基金：基于混合差分进化的优化调度研究。(负责人) (2009.1~2010.12)
[14]国家重点研发计划(2016YFB0901900)：能源互联网的规划、运行与交易基础理论。(课题一负责人) (2016.07~2020.06)
[15]973计划课题(2013CB329503)：面向脑信息编解码的机器学习方法。(骨干) (2013.01~2017.12)
[16]973计划课题(2009CB320602)：复杂生产制造全流程基于数据和知识的实时智能运行优化理论和方法研究。(骨干) (2011.01~2013.08)
[17]973计划课题(2002CB312203)：复杂生产制造过程实时、智能控制与优化理论和方法研究。(骨干) (2002.12~2008.5)
[18]863计划项目(2007AA04Z155)：流程工业企业生产过程的智能计划与动态优化调度技术。(副组长) (2008.1~2009.12)
[19]国家科技重大专项(2011ZX02504-008)：集成电路生产线智能调度与质量优化控制技术研究。(骨干) (2011.1~2013.12)
[20] 美团合作项目：即时配送多目标鲁棒调度场景的优化问题研究。(负责人) (2019~2020)
[1] 2015年度国家杰出青年科学基金[2] 2009年度教育部新世纪优秀人才支持计划[3] 2009年度清华大学学术新人奖[4] 2004年度北京市科技新星[5] 2010年度Scopus青年科学之星新人奖[6] 2016年度中国自动化学会青年科学家奖[7] 2014年度国家自然科学奖二等奖[8] 2019年度中国仿真学会科学技术奖一等奖[9] 2017年度云南省自然科学奖三等奖[10] 2011年度中国电子学会电子信息科学技术奖二等奖[11] 2008年度北京市科学技术奖三等奖[12] 2007年度高等学校科学技术奖自然科学奖二等奖[13] 2003年度教育部提名国家自然科学一等奖[14] 2017年度《控制与决策》优秀论文奖[15] 2016年度《控制理论与应用》优秀论文奖[16] 2014年度《自动化学报》优秀论文奖[17] 领跑者5000, 中国精品科技期刊顶尖学术论文, 证书编号(13857): S001200812001[18] 领跑者5000, 中国精品科技期刊顶尖学术论文, 证书编号(7597): S026201203014[19] 领跑者5000, 中国精品科技期刊顶尖学术论文, 证书编号(10418): R060201402004[20] 北京地区广受关注学术论文, 2020[21] 2005-2010 Engineering Applications of Artificial Intelligence Top Cited Article Awarded by Elsevier[22] 2019’ IEEE TEVC Outstanding Associate Editor[23] 2019年度《控制理论与应用》优秀编委奖[24] 2018’ IEEE ICIC Outstanding Leadership Award[25] IEEE国际智能计算会议最佳论文奖, ICIC’2018[26] 国际和声搜索算法会议最佳论文奖, ICHSA’2015[27] 中国过程控制年会Poster论文奖, CPCC’2014[28] 高等计算智能与智能信息国际会议最佳论文奖, IWACIII’2013[29] IEEE国际智能计算会议最佳论文奖, ICIC’2011[30] Finalist for Zhang Si-Ying Outstanding Youth Paper Award, CCDC’2010[31] IET咨询与控制技术国际会议优秀论文, ICT’2006[32] IEEE机器学习和控制论国际会议优秀论文奖, ICMLC’2002[33] 中国控制与决策年会优秀论文, CCDC’2004[34] 清华大学优秀博士论文一等奖[35] 清华大学优秀教材二等奖 (2004, 2008, 2012, 2016, 2020)[36] 清华大学第14届良师益友 (2014)[37] 清华大学第17届良师益友 (2020)[38] 清华大学优秀班主任一等奖 (2004, 2005)
主要论著：
[1] 王凌, 王圣尧, 方晨. 分布估计调度算法. 北京: 清华大学出版社, 2017[2] 王凌, 钱斌. 混合差分进化与调度算法. 北京: 清华大学出版社, 2012.[3] 王凌, 刘波. 微粒群优化与调度算法. 北京: 清华大学出版社, 2008.[4] 王京春, 王凌, 金以慧 (译). 过程的动态特性与控制. 北京: 电子工业出版社, 2006.[5] 王凌. 车间调度及其遗传算法. 北京: 清华大学&Springer出版社, 2003.[6] 王凌. 智能优化算法及其应用. 北京: 清华大学&Springer出版社, 2001.[7] Wu CG, Li W, Wang L, Zomaya AY. Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Transactions on Cloud Computing. (Regular Paper).[8] Wang JJ, Wang L. A bi-population cooperative memetic algorithm for distributed hybrid flow-shop scheduling. IEEE Transactions on Emerging Topics in Computational Intelligence. (Regular Paper).[9] Pan ZX, Lei DM, Wang L. A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling. IEEE Transactions on Cybernetics. (Regular Paper).[10] Pan QK, Gao L, Wang L. An effective cooperative co-evolutionary algorithm for distributed flowshop group scheduling problems. IEEE Transactions on Cybernetics. (Regular Paper).[11] Zhao FQ, He X, Wang L. A two-stage cooperative evolutionary algorithm with problem-specific knowledge for energy-efficient scheduling of no-wait flow-shop problem. IEEE Transactions on Cybernetics. (Regular Paper).[12] Wu XL, Yuan Q, Wang L. Multi-objective differential evolution algorithm for solving robotic cell scheduling problem with batch-processing machines. IEEE Transactions on Automation Science and Engineering. (Regular Paper).[13] He YM, Wu GH, Chen YW, Pedrycz W, Wang L, Xing LN. A generic Markov decision process model and reinforcement learning method for scheduling agile earth observation satellites. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).[14] Deng M, Liu BJ, Li SM, Du RH, Wu GH, Li HF, Wang L. A two-phase coordinated planning approach for heterogeneous earth-observation resources to monitor area targets. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).[15] Zhou SC, Xing LN, Zheng X, Du N, Wang L, Zhang QF. A self-adaptive differential evolution algorithm for scheduling a single batch-processing machine with arbitrary job sizes and release times. IEEE Transactions on Cybernetics. (Regular Paper).[16] Xu BL, Shi J, Lu ML, Cong JL, Wang L, Nener B. An automated cell tracking approach with multi-Bernoulli filtering and ant colony labor division. IEEE/ACM Transactions on Computational Biology and Bioinformatics. (Regular Paper).[17] Du YH, Wang L, Xing LN, Yan JG, Cai MS. Data-driven heuristic assisted memetic algorithm for efficient inter-satellite link scheduling in the BeiDou navigation satellite system. IEEE/CAA Journal of Automatica Sinica.[18] Wu CG, Li W, Wang L, Zomaya A. An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing. Future Generation Computer Systems, 2021, 117: 498-509.[19] Wang X, Wang L, Wang SY, Chen JF, Wu CG. An XGBoost-enhanced fast constructive algorithm for food delivery route planning problem. Computers & Industrial Engineering, 2021, 152: 107029.[20] Wu JY, Gong WY, Wang L. A clustering-based differential evolution with different crowding factors for nonlinear equations system. Applied Soft Computing, 2021, 98: 106733.[21] Wang R, Ma WB, Tan M, Wu GH, Wang L, Gong DW, Xiong J. Preference-inspired coevolutionary algorithm with active diversity strategy for multi-objective multi-modal optimization. Information Sciences, 2021, 546: 1148-1165.[22] Wang JJ, Wang L. A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(5): 1805-1819. (Regular Paper).[23] Li JQ, Song MX, Wang L, Duan PY, Han YY, Sang HY, Pan QK. Hybrid artificial bee colony algorithm for a parallel batching distributed flow shop problem with deteriorating jobs. IEEE Transactions on Cybernetics, 2020, 50(6): 2425-2439. (Regular Paper).
[24] Chang F, Dong MY, Liu M, Wang L, Duan YQ. A lightweight appearance quality assessment system based on parallel deep learning for painted car-body. IEEE Transactions on Instrumentation & Measurement, 2020, 69(8): 5298-5307. (Regular Paper).[25] Chen HK, Tian Y, Pedrycz W, Wu GH, Wang R, Wang L. Hyperplane assisted evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Cybernetics, 2020, 50(7): 3367-3380. (Regular Paper).[26] Liu QF, Gehrlein WV, Wang L, Yan Y, Cao YY, Chen W, Li Y. Paradoxes in numerical comparison of optimization algorithms. IEEE Transactions on Evolutionary Computation, 2020, 24(4): 777-791. (Regular Paper).[27] Du YH, Wang T, Xin B, Wang L, Chen YG, Xing LN. A data-driven parallel scheduling approach for multiple agile earth observation satellites based on cooperative neuro evolution of augmenting topologies. IEEE Transactions on Evolutionary Computation2020, 24(4): 679-693. (Regular Paper).[28] Rong M, Gong DW, Pedrycz W, Wang L. A multi-model prediction method for dynamic multi-objective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2020, 24(2): 290-304. (Regular Paper).[29] Liao ZW, Gong WY, Yan XS, Wang L, Hu CY. Solving nonlinear equations system with dynamic repulsion-based evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(4): 1590-1601. (Regular Paper).[30] Gong WY, Wang Y, Cai ZH, Wang L. Finding multiple roots of nonlinear equation systems via a repulsion-based adaptive differential evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(4): 1499-1513. (Regular Paper).[31] Jiang ED, Wang L, Peng ZP. Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm and Evolutionary Computation, 2020, 58: 100745.[32] Jiang ED, Wang L. Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices. Knowledge-Based Systems, 2020, 204: 106177.[33] Gao KZ, Huang Y, Sadollah A, Wang L. A review of energy-efficient scheduling in intelligent production systems. Complex & Intelligent Systems, 2020, 6(2): 237-249.[34] Sun BQ, Wang L, Peng ZP. Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing. Computers & Industrial Engineering, 2020, 146: 106604.[35] Zheng J, Wang L, Wang JJ. A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop. Knowledge-Based Systems, 2020, 194: 105536.[36] Li SJ, Gong WY, Wang L, Yan XS, Hu CY. A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models. Energy Conversion and Management, 2020, 225: 113474.[37] Li SJ, Gong WY, Wang L, Yan XS, Hu CY. Optimal power flow by means of improved adaptive differential evolution. Energy, 2020, 198: 117314.[38] Wang R, Xiong J, He MF, Gao L, Wang L. Multi-objective optimal design of hybrid renewable energy system under multiple scenarios. Renewable Energy, 2020, 151: 226-237.[39] Yan XS, Li PP, Tang K, Gao L, Wang L. Clonal selection based intelligent parameter inversion algorithm for prestack seismic data. Information Sciences, 2020, 517: 86-99.[40] Hu CY, Dai LG, Yan XS, Gong WY, Liu XB, Wang L. Modified NSGA-III for sensor placement in water distribution system. Information Sciences, 2020, 509: 488-500.[41] Chen JF, Wang L, Peng ZP. A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling. Swarm and Evolutionary Computation, 2019, 50: 100557.[42] Sun BQ, Wang L. A decomposition-based matheuristic for supply chain network design with assembly line balancing. Computers & Industrial Engineering, 2019, 131: 408-417.[43] Wang JJ, Wang L. Decoding methods for the flow shop scheduling with peak power consumption constraints. International Journal of Production Research, 2019, 57(10): 3200-3218.[44] Xiang S, Xing LN, Wang L, Zou K. Comprehensive learning pigeon-inspired optimization with tabu list. SCIENCE CHINA Information Sciences, 2019, 62(7): 070208.[45] Lei DM, Li M, Wang L. A two-phase meta-heuristic for multi-objective flexible job shop scheduling problem with total energy consumption threshold. IEEE Transactions on Cybernetics, 2019, 49(3): 1097-1109. (Regular Paper).[46] Wang L, Lu JW. A memetic algorithm with competition for the capacitated green vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 2019, 6(2): 516-526.[47] Jiang ED, Wang L. An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time. International Journal of Production Research, 2019, 57(6): 1756-1771.[48] Zhang JW, Wang L, Xing LN. Large-scale medical examination scheduling technology based on intelligent optimization. Journal of Combinatorial Optimization, 2019, 37(1): 385-404.[49] Li SJ, Gong WY, Yan XS, Hu CY, Bai DY, Wang L. Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy, 2019, 190: 465-474.[50] Zheng XL, Wang L. A collaborative multi-objective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(5): 790-800. (Regular Paper).[51] Wang Y, Shi JM, Wang R, Liu Z, Wang L. Siting and sizing of fast charging stations in highway network with budget constraint. Applied Energy, 2018, 228: 1255-1271.[52] Gong WY, Yan XS, Hu CY, Wang L, Gao L. Fast and accurate parameter extraction for different types of fuel cells with decomposition and nature-inspired optimization method. Energy Conversion and Management, 2018, 174: 913-921.[53] Wang R, Lai SM, Wu GH, Xing LN, Wang L, Ishibuchi H. Multi-clustering via evolutionary multi-objective optimization. Information Sciences, 2018, 450: 128-140.[54] Wu CG, Wang L. A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system. Journal of Parallel and Distributed Computing, 2018, 117: 63-72.[55] Wang L, Zheng XL. A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem. Swarm and Evolutionary Computation, 2018, 38: 54-63.[56] Gao KZ, Wang L, Luo JP, Jiang H, Sadollah A, Pan QK. Discrete harmony search algorithm for scheduling and rescheduling the re-processing problems in remanufacturing: A case study. Engineering Optimization, 2018, 50(6): 965-981.[57] Wang R, Li GZ, Ming MJ, Wu GH, Wang L. An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system. Energy, 2017, 141: 2288-2299.[58] Zheng HY, Wang L, Zheng XL. Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem. Soft Computing, 2017, 21(6): 1537-1548.[59] Deng J, Wang L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation, 2017, 32: 121-131.[60] Zheng XL, Wang L. A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. International Journal of Production Research, 2016, 54(18): 5554-5566.[61] Tian MM, Jiang YH, Gao XY, Wang L, Huang DX. Plantwide scheduling model for the typical polyvinyl chloride production by calcium carbide method. Industrial & Engineering Chemistry Research, 2016, 55(21): 6161-6174.[62] Zheng XL, Wang L. A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints. Expert Systems with Applications, 2016, 65: 28-39.[63] Wang L, Wang SY, Zheng XL. A hybrid estimation of distribution algorithm for unrelated parallel machine scheduling with sequence-dependent setup times. IEEE/CAA Journal of Automatica Sinica, 2016, 3(3): 235-246.[64] Shen JN, Wang L, Zheng HY. A modified teaching-learning-based optimization algorithm for bi-objective re-entrant hybrid flowshop scheduling. International Journal of Production Research, 2016, 54(12): 3622-3639.[65] Deng J, Wang L, Wang SY, Zheng XL. A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem. International Journal of Production Research, 2016, 54(12): 3561-3577.[66] Wang SY, Wang L. An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(1): 139-149. (Regular Paper).[67] Shi L, Jiang YH, Wang L, Huang DX. Efficient Lagrangian decomposition approach for solving refinery production scheduling problems involving operational transitions of mode switching. Industrial & Engineering Chemistry Research, 2015, 54(25): 6508-6526.[68] Zheng HY, Wang L. Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm. International Journal of Production Economics, 2015, 164: 421-432.[69] Wang SY, Wang L, Liu M, Xu Y. An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated Manufacturing, 2015, 28(3): 307-320.[70] Zheng HY, Wang L. An effective teaching-learning-based optimization algorithm for RCPSP with ordinal interval numbers. International Journal of Production Research, 2015, 53(6): 1777-1790.[71] Zhang X, Chen MY, Wang L, Peng ZH, Zhou DH. Connection-graph-based event-triggered output consensus in multi-agent systems with time-varying couplings. IET Control Theory and Applications, 2015, 9(1): 1-9.[72] Shi L, Jiang YH, Wang L, Huang DX. Refinery production scheduling involving operational transitions of mode switching under predictive control system. Industrial & Engineering Chemistry Research, 2014, 53(19): 8155-8170.[73] Pan QK, Wang L, Li JQ, Duan JH. A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimization. OMEGA-International Journal of Management Science, 2014, 45: 42-56.[74] Wang L, Fang C, Mu CD, Liu M. A Pareto-archived estimation-of-distribution algorithm for multi-objective resource-constrained project scheduling problem. IEEE Transactions on Engineering Management, 2013, 60(3): 617-626. (Regular Paper).[75] Wang SY, Wang L, Liu M, Xu Y. An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. International Journal of Production Economics, 2013, 145(1): 387-396.[76] Pan QK, Wang L, Sang HY, Li JQ, Liu M. A high performing memetic algorithm for the flowshop scheduling problem with blocking. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 741-756. (Regular Paper).[77] Wang L, Zhou G, Xu Y, Liu M. A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem. International Journal of Production Research, 2013, 51(12): 3593-3608.[78] Wang L, Wang SY, Liu M. A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem. International Journal of Production Research, 2013, 51(12): 3574-3592.[79] Wang L, Zheng XL, Wang SY. A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 2013, 48: 17-23.[80] Pan QK, Wang L, Mao K, Zhao JH, Zhang M. An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Transactions on Automation Science and Engineering, 2013, 10(2): 307-322. (Regular Paper).[81] Wang L, Wang SY, Xu Y, Zhou G, Liu M. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 2012, 62(4): 917-926.[82] Fang C, Wang L. An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(5): 890-901.[83] Pan QK, Wang L. Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. OMEGA-International Journal of Management Science, 2012, 40(2): 218-229.[84] Wang L, Fang C. An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(2): 449-460.[85] Wang L, Li LP. Fixed-structure H∞ controller synthesis based on differential evolution with level comparison. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 120-129. (Regular paper)[86] Wang L, Fang C. An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem. Information Sciences, 2011, 181(20): 4804-4822.[87] Liu B, Wang L, Liu Y, Wang SY. A unified framework for population-based metaheuristics. Annals of Operations Research, 2011, 186(1): 231-262.[88] Wang L, Pan QK, Tasgetiren MF. A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Computers & Industrial Engineering, 2011, 61(1): 76-83.[89] Pan QK, Wang L, Gao L, Li WD. An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Information Sciences, 2011, 181(3): 668-685.[90] Pan QK, Suganthan PN, Wang L, Gao L, Mallipeddi R. A differential evolution algorithm with self-adapting strategy and control parameters. Computers & Operations Research, 2011, 38(1): 394-408.[91] Wang L, Li LP. An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 2010, 41(6): 947-963.[92] Liu B, Wang L, Liu Y, Qian B, Jin YH. An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. Computers & Chemical Engineering, 2010, 34(4): 518-528.[93] Wang L, Huang FZ. Parameter analysis based on stochastic model for differential evolution algorithm. Applied Mathematics and Computation, 2010, 217(7): 3263-3273.[94] Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM. A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Computers & Operations Research, 2010, 37(3): 509-520.[95] Qian B, Wang L, Hu R, Huang DX, Wang X. A DE-based approach to no-wait flow-shop scheduling. Computers & Industrial Engineering, 2009, 57(3): 787-805.[96] Qian B, Wang L, Huang DX, Wang X. Multi-objective no-wait flow-shop scheduling with a memetic algorithm based on differential evolution. Soft Computing, 2009, 13(8-9): 847-869.[97] Pan QK, Wang L, Qian B. A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Computers & Operations Research, 2009, 36(8): 2498-2511.[98] Qian B, Wang L, Huang DX, Wang X. An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers. International Journal of Production Research, 2009, 47(1): 1-24.[99] Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research, 2009, 36(1): 209-233.[100] Li BB, Wang L, Liu B. An effective PSO-based hybrid algorithm for multi-objective permutation flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 2008, 38(4): 818-831. (Regular paper)[101] Liu B, Wang L, Jin YH. An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Computers & Operations Research, 2008, 35(9): 2791-2806.[102] Li BB, Wang L. A hybrid quantum-inspired genetic algorithm for multi-objective flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(3): 576-591. (Regular paper).[103] Liu B, Wang L, Jin YH. An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(1): 18-27. (Regular paper). (ESI)[104] He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 2007, 20(1): 89-99. (ESI)[105] Wang L, Zhang L, Zheng DZ. An effective hybrid genetic algorithm for flow shop scheduling with limited buffers. Computers & Operations Research, 2006, 33(10): 2960-2971.[106] Liu B, Wang L, Jin YH, Tang F, Huang DX. Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals, 2005, 25(5): 1261-1271. (ESI)[107] Wang L, Zheng DZ. An effective hybrid heuristic for flow shop scheduling. International Journal of Advanced Manufacturing Technology, 2003, 21(1): 38-44.[108] Jiang YH, Wang L, Jin YH. Bottleneck analysis for network flow model. Advances in Engineering Software, 2003, 34(10): 641-651.[109] Zhou T, Wang L, Sun ZS. Closed-loop model set validation under a stochastic framework. Automatica, 2002, 38(9): 1449-1461.[110] Wang L, Zheng DZ. An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research, 2001, 28(6): 585-596.
开设课程
[1]智能优化算法及其应用 (本科生课程)
[2]自动控制原理 (本科生课程) [国家精品课程] [北京市精品课程]
[3]生产调度及其智能优化 (研究生课程)
[4]人工神经网络 (研究生课程)
[5]文献检索与论文写作 (工程硕士课程)
博士生:刘波、钱斌 [清华大学优秀博士论文]、方晨、许烨 [清华大学优秀博士论文]、王圣尧 [清华大学优秀博士论文]、施磊、郑晓龙 [清华大学优秀博士论文]、张旭、沈婧楠、吴楚格、蒋恩达、陈靖方、王兴、王晶晶、郑洁、潘子肖、吴玉婷
硕士生:张亮 [清华大学优秀硕士论文]、潘晖、李彬彬 [清华大学优秀硕士论文]、何锲 [清华大学优秀硕士论文]、黄付卓、李灵坡 [清华大学优秀硕士论文]、周刚 [清华大学优秀硕士论文]、张鹏、郑环宇 [清华大学优秀硕士论文]、邓瑾 [清华大学优秀硕士论文]、王晶晶 [清华大学优秀硕士论文]、陆佳文 [清华大学优秀硕士论文]、孙斌奇 [清华大学优秀硕士论文]
