一、个人简介

孙超利，教授，博导，现任职于美高梅官网4688美高梅官网4688。

通讯地址：山西省太原市万柏林区窊流路66号，030024

E-mail: chaoli.sun.cn@gmail.com; chaoli.sun@tyust.edu.cn

Homepage:http://www.dscil.cn

二、学习经历:

2007/09--2011/06美高梅官网4688机械工程学院获工学博士学位

2000/09--2003/04河海大学计算机及信息工程学院获工学硕士学位

1996/09--2000/07河海大学计算机及信息工程学院获工学学士学位

三、工作经历:

2018/08—至今英国埃克塞特大学计算机学院访问学者

2017/12--至今美高梅官网4688美高梅官网4688教授

2011/08—2017/11美高梅官网4688美高梅官网4688副教授

2014/09--2016/09英国萨里大学计算机学院博士后

2012/10--2013/03英国萨里大学计算机学院访问学者

2009/03--2009/06台湾高雄应用科技大学电子学院学术交流

2006/09--2011/07美高梅官网4688美高梅官网4688讲师

2003/04--2006/08美高梅官网4688美高梅官网4688助教

四、研究领域:

计算智能，机器学习，代理模型辅助的进化优化及这些优化方法在实际工程中的应用

五、专业组织活动:

（1）IEEE计算智能协会Intelligent Systems Applications Technical Committee (ISATC)委员

（2）IEEE计算智能协会Evolutionary Computation Technical Committee (ECTC) 委员

（3）IEEE计算智能协会Emergent Technologies Technical Committee (ETTC) 委员

（4）ACM太原分会常务理事

（5）CCF太原分部副秘书长

（6）中国人工智能学会机器博弈专业委员会委员

（7）中国自动化学会大数据专业委员会委员

（8）中国自动化学会建筑机器人专业委员会委员

（9）IEEE计算智能协会进化计算技术委员会数据驱动的复杂进化优化小组主席

（10）IEEE计算智能系列研讨会(IEEE SSCI 2016 - IEEE SSCI 2019)基于模型

（11）进化算法分会数据驱动的复杂进化优化专题会主席

（12）IEEE进化计算会议(IEEE CEC 2017 - IEEE CEC 2020)基于模型进化算法分会数据驱动的复杂进化优化专题会主席

（13）GECCO’2016-2019 程序委员会委员

六、其它专业服务:

（1）Soft Computing副编辑

（2）Complex & Intelligent Systems编委

（3）Memetic Computing编委

（4）IEEE Transactions on Evolutionary Computation审稿人

（5）IEEE Transactions on Cybernetics审稿人

（6）IEEE Transactions on Fuzzy Systems审稿人

（7）IEEE Transactions on Cognitive and Developmental Systems审稿人

（8）IEEE Transactions on Emerging Topics and Computational Intelligence审稿人

（9）Swarm Intelligence审稿人

（10）Information Sciences审稿人、

（11）Natural Computing审稿人

（12）Swarm and Evolutionary Computation审稿人

（13）Applied Soft Computing审稿人

七、科研项目:

[1]数据驱动的高维复杂进化优化方法研究，国家自然科学基金面上项目，项目编号：61876123，2019年1月至2022年12月，主持（在研）


[2]代理模型辅助的优化算法在复杂多目标优化问题中的应用研究，山西省自然科学基金，项目编号：201801D121131，2018年12月至2020年12月，主持（结题）

[3]代理模型辅助的优化算法在复杂高维问题中的应用研究，山西省留学回国人员科技活动择优资助项目，2017年11月至2020年10月，主持（结题）

[4]数据驱动的复杂系统进化优化，山西省平台基地和人才专项优秀人才科技创新项目，项目编号：201805D211028，第二参与人（在研）

[5]求解计算费时约束优化问题的进化算法研究，山西省自然科学基金，项目编号：201901D111262，第二参与人（在研）

[6]代理模型辅助的动态车辆调度问题优化方法研究，山西省自然科学基金，项目编号：201901D111264，第二参与人（在研）

[7]基因变异临床诊断数据库，浙江天悟智能技术有限公司，2018年5月至2020年4月，主持（结题）

[8]结合先进机器学习方法的代理模型进化算法研究，国家青年基金，项目编号：61403272，2015年1月至2017年12月，主持（结题）

[9]数据驱动的多目标进化优化算法研究，东北大学流程工业综合自动化国家重点实验室开放课题，2015年1月至2017年12月，主持（结题）

[10]面向复杂机械系统优化设计的群体智能优化算法研究，山西省青年基金，项目编号：2011021019-3，2011年1月至2013年12月，主持（结题）

[11]微粒群算法预测策略的研究，美高梅官网4688博士启动基金，项目编号：20122010，2012年1月至2014年12月，主持（结题）

八、教研项目:

[1]以计算机博弈比赛为载体的创新人才培养模式研究，山西省教改项目，项目编号：J2014074，2014年7月至2016年7月，主持（结题）

[2]计算机博弈系统中随机搜索算法的应用和研究, 山西省高等学校大学生创新创业训练项目，项目编号：2014273，2014年7月至2016年7月，指导教师

[3]亚马逊棋计算机博弈系统，山西省高等学校大学生创新创业训练项目，项目编号：2012229，2012年7月至2013年7月，指导教师

九、发表论著:

专著/章节:

[1]Y. Jin, H. Wang,C. Sun, Data-Driven Evolutionary Optimization, Springer, 2021.

[2]T. Chugh,C. Sun, H. Wang, Y. Jin, Surrogate-Assisted Evolutionary Optimization of Large Problems. In: Bartz-Beielstein T., Filipič B., Korošec P., Talbi EG. (eds) High-Performance Simulation-Based Optimization. Studies in Computational Intelligence, vol 833. Springer, Cham, 2020.

[3]孙超利，面向机械系统优化设计的微粒群算法，机械工业出版社，2012。

期刊论文:

[1]孙超利，李贞，金耀初，模型辅助的计算费时进化高维多目标优化，自动化学报，2021，录用.

[2]孙超利，李婵，秦淑芬，张国晨，李晓波，基于不确定度采样准则的费时问题优化算法，控制与决策，2021，录用.

[3]于成龙，付国霞，孙超利，张国晨，全局/局部模型交替优化辅助的差分进化算法，计算机工程，2021，录用.

[4]Shufen Qin,Chaoli Sun, Yaochu Jin, Ying Tan, Jonathan Fieldsend, Large-scale Evolutionary Multi-objective Optimization Assisted by Directed Sampling, IEEE Transactions on Evolutionary Computation, 2021, accepted.

[5]Zhihai Ren,Chaoli Sun, Ying Tan, Guochen Zhang, Shufen Qin, A Bi-stage Surrogate-assisted Hybrid Algorithm for Expensive Optimization Problems, C omplex & Intelligent Systems, 2021, 7, 1391-1405.

[6]Hao Wang,Chaoli Sun, Guochen Zhang, Jonathan E. Fieldsend, Yaochu Jin, Non-dominated Sorting on Performance Indicators for Evolutionary Many-objective Optimization, Information Sciences, 2021, 551, 23-38.

[7]Yi Zhao,Chaoli Sun, Jianchao Zeng, Ying Tan, Guochen Zhang,A Surrogate-ensemble Assisted Expensive Many-objective Optimization, Knowledge-Based Systems, 2021, 211, 106520.

[8]Peng Liao,Chaoli Sun, Guochen Zhang, Yaochu Jin, Multi-surrogate Multi-tasking Optimization of Expensive problems, Knowledge-Based Systems, 2020, 205, 106262.

[9]Hao Wang, Mengnan Liang, Chaoli Sun, Guochen Zhang, Liping Xie, Multiple-strategy learning particle swarm optimization for large-scale optimization problems, Complex & Intelligent Systems, 2021, 7(1), 1-16.

[10]Shufen Qin, Chaoli Sun, Guochen Zhang, Xiaojuan He, Ying Tan, A modified particle swarm optimization based on decomposition with different ideal points for many-objective optimization problems, Complex & Intelligent Systems, 2020, 6(2), 263-274.

[11]田杰，孙超利，谭瑛，曾建潮，基于多点加点准则的代理模型辅助社会学习微粒群算法，控制与决策，2020，35(1)，131-138.

[12]Jie Tian, Ying Tan, Jianchao Zeng, Chaoli Sun, Yaochu Jin, Multi-objective Infill Criterion Driven Gaussian Process Assisted Particle Swarm Optimization of High-dimensional Expensive Problems, IEEE Transactions on Evolutionary Computation, 2019, 23(3), 459-472.

[13]Haibo Yu, Ying Tan, Jianchao Zeng, Chaoli Sun, A generation-based optimal restart strategy for surrogate-assisted social learning particle swarm optimization, Knowledge-Based Systems, 2019, 163(1), pp. 14-25.

[14]Haibo Yu, Ying Tan,Chaoli Sun, Jianchao Zeng, A comparison of quality measures for model selection in surrogate assisted evolutionary algorithm, Soft Computing, 2019, 23(23), pp. 12417-12436.

[15]Handing Wang, Yaochu Jin,Chaoli Sun, John Doherty,Offline data-driven evolutionary optimization using selective surrogate ensembles, IEEE Transactions on Evolutionary Computation, 2018, 23(2), pp. 203-216.

[16]Haibo Yu, Ying Tan, Jianchao Zeng,Chaoli Sun, Yaochu Jin, Surrogate-assisted Hierarchical Particle Swarm Optimization, Information Sciences, 2018, 454-455, pp. 59-72.

[17]Chaoli Sun, Yaochu Jin, Jinliang Ding, Jianchao Zeng,A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems, Memetic Computing, 2018, 10(2), pp. 123-134.

[18]Chaoli Sun, Yaochu Jin, Ran Cheng, Jinliang Ding, Jianchao Zeng,Surrogate-assisted Cooperative Swarm Optimization of High-dimensional Expensive Problems, IEEE Transactions on Evolutionary Computation, 2017, 21(4), 644-660.

[19]孙超利，郭一娜，谭瑛，径向基函数神经网络辅助的微粒群算法，美高梅官网4688学报，2017, 38(3), pp. 178-184.

[20]Chaoli Sun, Yaochu Jin, Jianchao Zeng, Yang Yu, A Two-layer Surrogate-assisted Particle Swarm Optimization Algorithm, Soft Computing, 2015, 19(6), pp. 1461-1475.

[21]刘彤，孙超利，曾建潮，微粒群进化估值策略在多目标优化中的应用，美高梅官网4688学报，2015, 36(5), pp. 338-347.

[22]Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Songdong Xue, Yaochu Jin, A New Fitness Estimation Strategy for Particle Swarm Optimization, Information Sciences, 2013, 221, pp. 355-370.

[23]Chaoli Sun, Jianchao Zeng, Jengshyang Pan, A Modified Particle Swarm Optimization with Feasibility-based Rules for Mixed-variable Optimization Problems, International Journal of Innovative Computing, Information and Control, 2011, 7(6), 3081-3096.

[24]Chaoli Sun, Jianchao Zeng, Jengshyang Pan, An Improved Vector Particle Swarm Optimization for Constrained Optimization Problems, Information Sciences, 2011, 181(6), 1153-1163.

[25]Chaoli Sun, Ying Tan, Jianchao Zeng, Jengshyang Pan, Yuanfang Tao, The Structure Optimization of Main Beam for Bridge Crane Based on An Improved PSO, Journal of Computers,2011,6(8), 1585-1590.

[26]Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Yuanfang Tao, Crank Block Steering Mechanism Optimization for Forklift Truck Based on Vector PSO, Advanced Materials Research, 2011, 145(43), 43-48.

会议论文:

[1] Guoxia Fu, Chaoli Sun, Ying Tan, Guochen Zhang, Yaochu Jin, A Surrogate-assisted Evolutionary Algorithm with Random Feature Selection for Large-scale Expensive Problems, 16th International Conference on Parallel Problem Solving from Nature (PPSN), 2020, accepted.

[2] Shufen Qin, Chaoli Sun, Yaochu Jin, Guochen Zhang, Bayesian Approaches to Surrogate-Assisted Evolutionary Multi-objective Optimization: A Comparative Study, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.12-6-9, Xiamen, China, 2019, pp. 2074-2080.

[3] Hao Wang,Chaoli Sun, Yaochu Jin, Shufen Qin, Haibo Yu, A Multi-indicator based Selection Strategy for Evolutionary Many-objective Optimization, 2019 IEEE Congress on Evolutionary Computation (CEC), 2019.6.10-13, Wellington, New Zealand, 2019, pp. 2043-2050.

[4] Shufen Qin,Chaoli Sun, Yaochu Jin, Lier Lan, Ying Tan, A New Selection Strategy for Decomposition-based Evolutionary Many-Objective Optimization, 2019 IEEE Congress on Evolutionary Computation (CEC), 2019.6.10-13, Wellington, New Zealand, 2019, pp. 2427-2434.

[5] Chaoli Sun, Yaochu Jin, Ying Tan, Semi-supervised Learning Assisted Particle Swarm Optimization of Computationally Expensive Problem, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18), 2018.7.15-19, Kyoto, Japan, 2018, pp. 45-52.

[6] Jie Tian, Ying Tan, Chaoli Sun, Jianchao Zeng, Yaochu Jin, Comparisons of Different Kernels in Kriging-Assisted Evolutionary Expensive Optimization, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017.11-27-12.1, Hawaii, USA, 2017, pp. 2402-2409.

[7] Haibo Yu, Ying Tan, Chaoli Sun, Jianchao Zeng, Clustering-based evolution control for surrogate-assisted particle swarm optimization, 2017 IEEE Congress on Evolutionary Computation (CEC), 2017.6.5-8, Spain, 2017, pp. 503-508.

[8] Haibo Yu,Chaoli Sun, Yin Tan, Jianchao Zeng, Yaochu Jin, An Adaptive Model Selection Strategy for Surrogate-Assisted Particle Swarm Optimization Algorithm, 2016 IEEE Symposium Series on Computational Intelligence, 2016.

[9] Jie Tian,Chaoli Sun, Yaochu Jin, Yin Tan, Jianchao Zeng, A Self-adaptive Similarity-based Fitness Approximation for Evolutionary Optimization, 2016 IEEE Symposium Series on Computational Intelligence, 2016.

[10] Qianqian Kong, Xiaojuan He,Chaoli Sun,A surrogate-assisted hybrid optimization algorithms for computational expensive problems, 12th World Congress on Intelligent Control and Automation (WCICA), 2016, pp. 2126-2130.

[11] Tong Liu, Chaoli Sun, Jianchao Zeng, Songdong Xue, Yaochu Jin, Similarity-and reliability-assisted fitness estimation for particle swarm optimization of expensive problems, 2014 IEEE Congress on Evolutionary Computation (CEC), 2014.7.6-11, Beijing, 2014, pp. 640-646.

[12] Ge Gao, Chaoli Sun, Jianchao Zeng, Songdong Xue, A constraintapproximation assisted PSO for computationally expensive constrained problems,11th World Congress on Intelligent Control and Automation (WCICA), 2014.6.29-7.4, Shenyang, 2015, pp. 1354-1359.

[13] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Yaochu Jin, Similarity-based Evolution Control for Fitness Estimation in Particle Swarm Optimization,2013 IEEE Symposium Series on Computational Intelligence, 2013.4.16-19, Singapore, 2013, pp. 1-8.

[14] Ran Cheng,Chaoli Sun, Yaochu Jin, A Multi-swarm Evolutionary Framework Based on a Feedback Mechanism, 2013 IEEE Congress on Evolutionary Computation, 2013.6.20-23, Cancun, Mexico, 2013, pp. 718-724.

[15] Yunqiang Zhang, Ying Tan,Chaoli Sun, Jianchao Zeng, A Hybrid Intelligent Algorithm for Mixed-variable Optimization Problems, 2011 International Conference on Future Communication, Computing, Control and Management, 2011.12.16-17, Phuket, Thailand, 2012, 141(1), pp. 249-256.

[16] Chaoli Sun, Jianchao Zeng, Shuchuan Chu, John F. Roddick, Jengshyang Pan, Solving Constrained Optimization Problems by An Improved Particle Swarm Optimization, 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, 2011.12.16-18, Shenzhen, China, 2011, pp. 124-128.

[17] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Yunqiang Zhang, PSO with Constraint-preserving Mechanism for Mixed-variable Optimization Problems, 1st International Conference on Robot, Vision and Signal Processing, 2011.11.21-23, Kaohsiung, Taiwan, 2011, pp. 149-153.

[18] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Shuchuan Chu, Yunqiang Zhang, Yunqiang Zhang,A Double Particle Swarm Optimization for Mixed-variable Optimization Problems, 3rd International Conference on Computational Collective Intelligence - Technologies and Applications, 2011.9.21-23, Gdynia, Poland, 2011, pp. 93-102.

[19] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Yuanfang Tao, Crank Block Steering Mechanism Optimization for Forklift Truck Based on PSO, 2nd International Conference on Computer Engineering and Technology, 2010.4.16-18, Chengdu, China, 2010, pp. 200-204.

[20] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, A New Vector Particle Swarm Optimization for Constrained Optimization Problems, 2009 International Joint Conference on Computational Sciences and Optimization, 2009.4.24-26, Sanya, Hainan Island, China, 2009, pp. 485-488.

[21] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, A Particle Swarm Optimization with Feasibility-based Rules for Mixed-variable Optimization Problems, 2009 Ninth International Conference on Hybrid Intelligent Systems, 2009.8.12-14, Shenyang, China, 2009, pp. 543-547.

[22] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, An Improved Particle Swarm Optimization with Feasibility-based Rules for Constrained Optimization Problems,22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2009.6.24-27, Tainan, Taiwan, 2009, pp. 202-211.

[23] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, An Improved Particle Swarm Optimization with Feasibility-based Rules for Mixed-variable Optimization Problems, 2009 Fourth International Conference on Innovative Computing, Information and Control, 2009.12.7-9, Kaohsiung, Taiwan, 2009, pp. 897-903.(EI)

[24] Chaoli Sun, Jianchao Zeng, Jengshyang Pan, A New Method for Constrained Optimization Problems to Produce Initial Values, 2009 Chinese Control and Decision Conference, 2009.6.17-19, Guilin, China, 2009, pp. 2690-2692.