一、個人簡介

孫超利，教授，博導，現任職于新澳门京葡萄威尼斯入口。

通訊地址：山西省太原市萬柏林區窊流路66号，030024

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

Homepage:http://www.dscil.cn

二、學習經曆:

2007/09--2011/06新澳门京葡萄威尼斯入口機械工程學院獲工學博士學位

2000/09--2003/04河海大學計算機及信息工程學院獲工學碩士學位

1996/09--2000/07河海大學計算機及信息工程學院獲工學學士學位

三、工作經曆:

2018/08—至今英國埃克塞特大學計算機學院訪問學者

2017/12--至今新澳门京葡萄威尼斯入口教授

2011/08—2017/11新澳门京葡萄威尼斯入口副教授

2014/09--2016/09英國薩裡大學計算機學院博士後

2012/10--2013/03英國薩裡大學計算機學院訪問學者

2009/03--2009/06台灣高雄應用科技大學電子學院學術交流

2006/09--2011/07新澳门京葡萄威尼斯入口講師

2003/04--2006/08新澳门京葡萄威尼斯入口助教

四、研究領域:

計算智能，機器學習，代理模型輔助的進化優化及這些優化方法在實際工程中的應用

五、專業組織活動:

（1）IEEE計算智能協會Intelligent Systems Applications Technical Committee (ISATC)委員

（2）IEEE計算智能協會Evolutionary Computation Technical Committee (ECTC) 委員

（3）IEEE計算智能協會Emergent Technologies Technical Committee (ETTC) 委員

（4）ACM太原分會常務理事

（5）CCF太原分部副秘書長

（6）中國人工智能學會機器博弈專業委員會委員

（7）中國自動化學會大數據專業委員會委員

（8）中國自動化學會建築機器人專業委員會委員

（9）IEEE計算智能協會進化計算技術委員會數據驅動的複雜進化優化小組主席

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

（11）進化算法分會數據驅動的複雜進化優化專題會主席

（12）IEEE進化計算會議(IEEE CEC 2017 - IEEE CEC 2020)基于模型進化算法分會數據驅動的複雜進化優化專題會主席

（13）GECCO’2016-2019 程序委員會委員

六、其它專業服務:

（1）Soft Computing副編輯

（2）Complex & Intelligent Systems編委

（3）Memetic Computing編委

（4）IEEE Transactions on Evolutionary Computation審稿人

（5）IEEE Transactions on Cybernetics審稿人

（6）IEEE Transactions on Fuzzy Systems審稿人

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

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

（9）Swarm Intelligence審稿人

（10）Information Sciences審稿人、

（11）Natural Computing審稿人

（12）Swarm and Evolutionary Computation審稿人

（13）Applied Soft Computing審稿人

七、科研項目:

[1]數據驅動的高維複雜進化優化方法研究，國家自然科學基金面上項目，項目編号：61876123，2019年1月至2022年12月，主持（在研）


[2]代理模型輔助的優化算法在複雜多目标優化問題中的應用研究，山西省自然科學基金，項目編号：201801D121131，2018年12月至2020年12月，主持（結題）

[3]代理模型輔助的優化算法在複雜高維問題中的應用研究，山西省留學回國人員科技活動擇優資助項目，2017年11月至2020年10月，主持（結題）

[4]數據驅動的複雜系統進化優化，山西省平台基地和人才專項優秀人才科技創新項目，項目編号：201805D211028，第二參與人（在研）

[5]求解計算費時約束優化問題的進化算法研究，山西省自然科學基金，項目編号：201901D111262，第二參與人（在研）

[6]代理模型輔助的動态車輛調度問題優化方法研究，山西省自然科學基金，項目編号：201901D111264，第二參與人（在研）

[7]基因變異臨床診斷數據庫，浙江天悟智能技術有限公司，2018年5月至2020年4月，主持（結題）

[8]結合先進機器學習方法的代理模型進化算法研究，國家青年基金，項目編号：61403272，2015年1月至2017年12月，主持（結題）

[9]數據驅動的多目标進化優化算法研究，東北大學流程工業綜合自動化國家重點實驗室開放課題，2015年1月至2017年12月，主持（結題）

[10]面向複雜機械系統優化設計的群體智能優化算法研究，山西省青年基金，項目編号：2011021019-3，2011年1月至2013年12月，主持（結題）

[11]微粒群算法預測策略的研究，新澳门京葡萄威尼斯入口博士啟動基金，項目編号：20122010，2012年1月至2014年12月，主持（結題）

八、教研項目:

[1]以計算機博弈比賽為載體的創新人才培養模式研究，山西省教改項目，項目編号：J2014074，2014年7月至2016年7月，主持（結題）

[2]計算機博弈系統中随機搜索算法的應用和研究, 山西省高等學校大學生創新創業訓練項目，項目編号：2014273，2014年7月至2016年7月，指導教師

[3]亞馬遜棋計算機博弈系統，山西省高等學校大學生創新創業訓練項目，項目編号：2012229，2012年7月至2013年7月，指導教師

九、發表論著:

專著/章節:

[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]孫超利，面向機械系統優化設計的微粒群算法，機械工業出版社，2012。

期刊論文:

[1]孫超利，李貞，金耀初，模型輔助的計算費時進化高維多目标優化，自動化學報，2021，錄用.

[2]孫超利，李婵，秦淑芬，張國晨，李曉波，基于不确定度采樣準則的費時問題優化算法，控制與決策，2021，錄用.

[3]于成龍，付國霞，孫超利，張國晨，全局/局部模型交替優化輔助的差分進化算法，計算機工程，2021，錄用.

[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]田傑，孫超利，譚瑛，曾建潮，基于多點加點準則的代理模型輔助社會學習微粒群算法，控制與決策，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]孫超利，郭一娜，譚瑛，徑向基函數神經網絡輔助的微粒群算法，新澳门京葡萄威尼斯入口學報，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]劉彤，孫超利，曾建潮，微粒群進化估值策略在多目标優化中的應用，新澳门京葡萄威尼斯入口學報，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.

會議論文:

[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.