个人信息
姓名:魏静萱 博士
职位:副教授 硕导
单位: 计算机学院

联系地址
联系地址：西安电子科技大学159信箱， 710071
邮箱：wjx@xidian.edu.cn
办公地址： 主楼一区429


简介
西安电子科技大学计算机学院副教授。于2003年在陕西师范大学获得应用数学学士学位，分别于2006年和2009年在西安电子科技大学获得应用数学硕士和博士学位。于2010年5月至2010年7月在澳大利亚南昆士兰大学做助理研究员，2010年10月至2011年10月在新西兰惠灵顿维多利亚大学做访问****。

研究领域
1．智能计算，多目标优化
2.粒子群优化
3．遗传算法
4．动态优化
5.优化理论与方法

教学
1组合数学(本科三年级 任选，2009年秋)
2. 代数系统(本科四年级 任选，2009年秋)
3. 组合数学(硕士研究生 学位，2012年秋)

科研项目
1. 国家自然科学基金青年基金项目：基于粒子群优化算法的不确定性多目标优化问题研究及其应用 (NSFC **, 2013.01-2015.12), 项目负责人
2. 留学回国基金：动态多目标优化问题研究 （2013—2015), 项目负责人
3. 中央高校基本科研业务费：动态环境下的网格安全性问题研究 (2010.09~2012.09), 项目负责人
4. 国家自然科学基金面上项目：基于大数据和云环境的两类关键问题优化建模与优化方法研究 （NSFC **, 2013.01-2016.12), 主要成员

所获奖励
1. 陕西高等学校科学技术一等奖：高效智能优化方法和理论 （排名第2）
2. 陕西省科学技术二等奖：智能优化方法与理论 (排名第2)

学术活动
1. IEEE Transactions on Evolutionary Computation 审稿人.
2. IEEE Transactions on Systems, Man and Cybernetics-Part B 审稿人
3. IEEE Conference on Evolutionary Computation 审稿人.
4. IEEE会员

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个人信息
姓名:魏静萱 博士
职位:副教授 硕导
单位: 计算机学院

联系地址
联系地址：西安电子科技大学159信箱， 710071
邮箱：wjx@xidian.edu.cn
办公地址： 主楼一区429


简介
西安电子科技大学计算机学院副教授。于2003年在陕西师范大学获得应用数学学士学位，分别于2006年和2009年在西安电子科技大学获得应用数学硕士和博士学位。于2010年5月至2010年7月在澳大利亚南昆士兰大学做助理研究员，2010年10月至2011年10月在新西兰惠灵顿维多利亚大学做访问****。

研究领域
1．智能计算，多目标优化
2.粒子群优化
3．遗传算法
4．动态优化
5.优化理论与方法

教学
1组合数学(本科三年级 任选，2009年秋)
2. 代数系统(本科四年级 任选，2009年秋)
3. 组合数学(硕士研究生 学位，2012年秋)

科研项目
1. 国家自然科学基金青年基金项目：基于粒子群优化算法的不确定性多目标优化问题研究及其应用 (NSFC **, 2013.01-2015.12), 项目负责人
2. 留学回国基金：动态多目标优化问题研究 （2013—2015), 项目负责人
3. 中央高校基本科研业务费：动态环境下的网格安全性问题研究 (2010.09~2012.09), 项目负责人
4. 国家自然科学基金面上项目：基于大数据和云环境的两类关键问题优化建模与优化方法研究 （NSFC **, 2013.01-2016.12), 主要成员

所获奖励
1. 陕西高等学校科学技术一等奖：高效智能优化方法和理论 （排名第2）
2. 陕西省科学技术二等奖：智能优化方法与理论 (排名第2)

学术活动
1. IEEE Transactions on Evolutionary Computation 审稿人.
2. IEEE Transactions on Systems, Man and Cybernetics-Part B 审稿人
3. IEEE Conference on Evolutionary Computation 审稿人.
4. IEEE会员

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研究课题
研究课题及研究项目
1. 不确定环境下的网格安全性问题研究
由于网格环境的不确定性和动态性等新特性使得应用程序在执行过程中可能会延迟或失效，这就使得应用程序的可信性成为一个新的必须考虑的因素。本项目从网格固有安全性和行为安全性两方面来考虑建立新的网格安全模型以满足网络安全性的需求。根据环境随时间变化的不同情形，研究相应的动态网格安全模型，并设计出求解动态优化问题的高效进化算法。
2. 高可信的多目标进化算法
在现实生活中，人们在设计方案规划时总体上反映了“最大化效益, 最小化成本”，这一基本优化原则，在合作对策问题中如何求解最优策略以获得共赢目标，在非合作对策中如何使自己的利益实现最大，对方受益最小等问题实际上都是一个多目标优化问题。在单目标优化问题中，最优解已具有了明确概念，但这一定义不能推广到多目标优化问题中。不同于单目标优化，多目标优化问题的最优解应是一组最优解的集合，称为非劣解集或Pareto最优解集。早在1896年法国经济学家V. Pareto就提出了这一观点，可是传统数学规划原理的多目标优化方法在实际优化问题中往往不太适用，因此，有必要研究求解多目标优化问题的高效算法。
3. 动态优化问题
在管理科学、运筹学、信息科学、系统科学、计算机科学以及工程等很多领域都存在人为的或客观的不确定性，如随机性、模糊性、粗糙性、随机模糊性。在不确定环境下如何建立优化模型、如何设计高效的算法求解这些模型，是十分重要的。本项目主要研究动态最优化问题并对其设计有效的进化算法。在许多问题中，目标函数不仅与决策变量有关，还会随着时间或环境而动态变化，这种优化问题便是动态最优化问题(Dynamic optimization problems)。 动态最优化问题是近几年进化算法领域的一个新的研究课题，正在引起越来越多研究者的兴趣。它分为动态单目标最优化问题和动态多目标最优化问题。
4. 多目标优化算法求解约束优化问题
在工程等实际问题中，经常遇到不可微带约束的问题。目前，还没有一种通用的传统优化方法，能够处理这种类型的约束。对于约束极小化问题来说，不仅要使目标函数值在迭代过程中不断减小，而且还要注意解的可行性。通常可采用如下思路去构造算法：将约束优化问题转为无约束优化问题、将非线性规划问题转化为线性规划问题、将复杂问题转化为简单问题。为了有效地求解该问题，人们将目光转向随机搜索算法，其中以进化算法(EA)为代表的仿生随机算法，以较强的求解能力受到广大****的青睐，并成为求解约束优化问题的重要工具。多目标类方法的特点是：即不使用传统的罚函数，也不区分可行解和不可行解。大多数算法将约束优化问题转化为两个目标优化问题，其中一个为原问题的目标函数，另一个为违反约束条件的程度函数。利用Pareto优于关系，定义个体Pareto序值以便对个体进行排序选优。
5. 粒子群优化(PSO)
粒子群算法(PSO)是计算智能领域，除蚁群算法外的另一种群智能算法。它同遗传算法类似，通过个体间的协作和竞争实现全局搜索。系统初始化为一组随机解，称之为粒子。通过粒子在搜索空间的飞行完成寻优，在数学公式中即为迭代，它没有遗传算法的交叉和变异算子,而是粒子在解空间追随最优的粒子进行搜索。

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Publication Records

Refereed Papers
Jingxuan Wei, Yuping Wang. An Infeasible Elitist Based Particle Swarm Optimization for Constrained Multi-objective Optimization and Its Convergence. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 2010, 24(3):381-400. (SCI
003) Jingxuan Wei, Yuping Wang. A Multi-objective Fuzzy Particle Swarm Optimization Based on Elite Archiving and Its Convergence. Journal of Systems Engineering and Electronics, 2008, 19(5):1035-1041.(SCI: 029)
Jingxuan Wei, Yuping Wang. A Hybrid Particle Swarm Optimization for Constrained Multi-objective Optimization. 2010, 29(5):1001-1018. Computing and Informatics. (SCI：001)
Jingxuan Wei, Yuping Wang. Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems.2012 IEEE Congress on Evolutionary Computation, Brisbane, Aus,2012:1-8. (EI, Top A conference)
Jingxuan Wei, Mengjie Zhang. A Memetic particle swarm optimization for constrained multi-objective optimization problems. 2011 IEEE Congress on Evolutionary Computation, New Orleans, USA: June 5-8,2011:1636-1643. (EI, Top A conference) Jingxuan Wei, Mengjie Zhang. Attraction based PSO with sphere search for dynamic constrained multi-objective optimizaiton problems. Proceedings of GECCO'11, Dublin, Ireland, July 12-16, 2011:77-78. (EI, Top A conference)
Jingxuan Wei, Mengjie Zhang. Simplex Model Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization. Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence. Lecture Notes in Artificial Intelligence. Perth, Australia, December 5-8, 2011:372-381.
Jingxuan Wei, Yuping Wang. A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems. Proceeding of the 6th International Conference, SEAL2006, Hefei, China: Springer-Verlag, 2006: 174-180. (SCI: 023)
Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han, Jingxuan Wei. A Clustering Multi-objective Evolutionary Algorithm Based on Orthogonal and Uniform Design. IEEE Conference on Evolutionary Computation CEC’09, 2927 - 2933. (EI,Top A conference)
Jingxuan Wei, Yuping Wang. A Dynamical Particle Swarm Algorithm with Dimension Mutation. Proceedings of the 2006 International Conference on Computational Intelligence and Security, CIS06, Guangzhou, China: IEEE Press, 2006, Vol.1: 254-257. (EI: )
Jingxuan Wei, Yuping Wang. A new model based Multi-Objective PSO Algorithm. Post Proceeding of the 2006 International Conference on Computational Intelligence and Security, CIS06, Guangzhou, China: Springer-Verlag.2007:87-94. (EI: )
Jingxuan Wei, Yuping Wang. A New Model based Hybrid PSO Algorithm for Multi-objective Problems. Proceeding of the The 3rd International Conference on Natural Computation (ICNC'07), Hainan, China: IEEE Press.2007, Vol. 3: 497-501.(EI: )
Jingxuan Wei, Yuping Wang. An Attraction Based Evolutionary Algorithm for Global Optimization Problems: The Workshop of 2005 International Conference on Computational Intelligence and Security; 2005: 63-66.
魏静萱， 王宇平. 求解约束优化问题的改进粒子群算法. 系统工程与电子技术. 2008, 30(4): 739-742. (EI: )
魏静萱，王宇平. 一种解决约束优化问题的模糊粒子群算法. 电子与信息学报. 2008, 30(5): 1218-1221.(EI: )
魏静萱，王宇平. 基于新模型的多目标Memetic 算法及收敛分析[J]. 控制理论与应用. 2008, 25(3): 389-392.(EI: )

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招生要求
~~~~~~~~~~~~~~~~~~~~~~~~~~
关于研究生招生的信息：欢迎本校和外校优秀本科毕业生报考硕士研究生。
对考生的基本要求：
1、具有强烈的求知欲望和坚定的科学奉献精神；
2、较强的工作责任心和踏实的工作作风；
3、良好的团队协作精神；
4、良好的理论基础、外语能力和数学功底。
要求报考的同学最好报考前联系我，做初步的面试。与我联系的最佳方式是发邮件。


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Profile

Name: Dr. Jingxuan Wei
Associate Professor
Department: School of Computer Science and Technology, Xidian University

Contact Information
Address: Mail box 179, School of Computer Science and Technology,

Xidian Uni. Xi'an, China.

Email: wjx@xidian.edu.cn

Address: Main Building I 429


Introduction
2010/10~2011/10: Visiting researcher with the Artificial Intelligence Research Group in the School of Engineering and Computer Science at Victoria University of Wellington.
2010/05~2010/07: Assistant researcher in theDepartment of Mathematics and Computing, University of Southern Queensland, Australia.
2012~so far: Associate Professor in the School of Computer Science, Xidian Uni., China
2009~2011: Lecturer in the School of Computer Science, Xidian Uni., China
2003~2009: Master and Ph.d in the School of Science, Xidian Uni., China. Major in Artificial Intelligence.
1999~2003: Bachelor of Science in the School of Science , Shaanxi Normal Uni., China. Major in applied mathematics.

Research Interests
1.More effective evolutionary multi-objective optimization algorithms
2.Optimization in dynamic environment
3.Evolutionary multiobjective approaches to constraint handling
4.Particle swarm optimization (PSO)

Teaching
1. Combinatoria Mathematics: Undergraduate students, Autumn, 2009.
2. Modern Algebra: Autumn, 2009.
3. Introduction of Artificial Intelligence: Semester 1, 2011 (Tutor)
4. Combinatoria Mathematics: Postergraduate students, Autumn, 2012.

Funding
I have been granted the following research fundings :
1. Research on uncertain multi-objective optimizatoin problems by PSO, supported by National Science Foundation of China. NSFC (No.**) 2013.01-2015.12. PI: Jingxuan Wei
2. Research On Grid Security Problems under Dynamic Environments, Fundamental Research Funds for the Central Universities. (No. **) 2010.9~2012.9. PI: Jingxuan Wei
3. Research on dynamic multi-objective optimization problems, supported by overseas returnee research funding. 2013-2015. PI: Jingxuan Wei

Professional activities
1. Reviewer for IEEE Transactions on Evolutionary Computation.
2. Reviewer for IEEE Transactions on Systems, Man and Cybernetics-Part B
3. Reviewer for IEEE Conference on Evolutionary Computation.
4. Member of IEEE

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Research
Our Research Projects and Research Fundings：
I am a staff member of the Evolutionary Computation Research Group (leader Prof. Yuping Wang), which is active in the following areas:
Research and Application of Computational algorithms and heuristics based on iterative population-based systems. This is inspired by the Darwinian principle of survival of fitness, which is the main driving force we use for searching for novel and better solutions to a wide range of problems. Similar as other inspired algorithms, such as particle swarm optimization, are included in the scope of computational intelligence. We focus on the following problems.
1. More effective evolutionary multi-objective optimization algorithms
Many real-world problems involve multiple measures of performance, or objectives, which should be optimized simultaneously, however optimal performance according to one objective often implies unacceptably low performance in one or more of the other objective dimensions. This requires a compromise to be reached. Evolutionary algorithms are naturally suitable to this type of problem solving because EA is population-based and it is possible to generate multiple feasible solutions in a single run. In this project, you are expected to study methods that allow for more efficient computation in EMO. Standard test functions will be used for conducting experiments and analysis of the results. Comparison with at least one of the existing MO algorithms should be provided.
2. Optimization in dynamic environment
Traditionally optimization is carried out towards a single static objective, which does not change during the course of the optimization. In recent years, there have been increasing interests in using distributed evolutionary algorithms to handle an optimization task that changes its optima over time. We could take advantage of the parallel and distributed structure of a parallel evolutionary algorithm to deal with this kind of task.
3. Evolutionary multiobjective approaches to constraint handling
Constrained optimization is optimization of an objective function subject to constraints on the possible values of the domain variables. Constraints can be either equality constraints or inequality constraints. Many real-world problems must be handled with careful consideration of their constraints on variables. Evolutionary Multiobjective Optimization (EMO) shows a great promise in providing alternative efficient constraint handling techniques to the traditional methods. In EMO, constraints can be treated as secondary objectives (hard or soft constraints). By using the Pareto approach (dominance comparison), it is possible to avoid the use of penalty functions or weighted sum methods. some existing works such as NSGA II (Deb, 2002) have shown competitive performance in comparison with the traditional constraint handling methods.
4.Particles Swarm Optimisation (PSO) is a relatively new technique that searches for optimal
inspiration from flocks of animals, e.g. birds, searching for a goal. Multiple potential solutions, termed particles, are evolved based on global and local information on the worth of discovered solutions. The swarm is kept cohesive by a topological relationship between the agents, which has a velocity component to quickly home in on promising regions of search.

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Papers
Refereed Papers
Jingxuan Wei, Yuping Wang. An Infeasible Elitist Based Particle Swarm Optimization for Constrained Multi-objective Optimization and Its Convergence. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 2010, 24(3):381-400. (SCI
003) Jingxuan Wei, Yuping Wang. A Multi-objective Fuzzy Particle Swarm Optimization Based on Elite Archiving and Its Convergence. Journal of Systems Engineering and Electronics, 2008, 19(5):1035-1041.(SCI: 029)
Jingxuan Wei, Yuping Wang. A Hybrid Particle Swarm Optimization for Constrained Multi-objective Optimization. 2010, 29(5):1001-1018. Computing and Informatics. (SCI：001)
Jingxuan Wei, Yuping Wang. Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems.2012 IEEE Congress on Evolutionary Computation, Brisbane, Aus,2012:1-8. (EI, Top A conference)
Jingxuan Wei, Mengjie Zhang. A Memetic particle swarm optimization for constrained multi-objective optimization problems. 2011 IEEE Congress on Evolutionary Computation, New Orleans, USA: June 5-8,2011:1636-1643. (EI, Top A conference) Jingxuan Wei, Mengjie Zhang. Attraction based PSO with sphere search for dynamic constrained multi-objective optimizaiton problems. Proceedings of GECCO'11, Dublin, Ireland, July 12-16, 2011:77-78. (EI, Top A conference)
Jingxuan Wei, Mengjie Zhang. Simplex Model Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization. Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence. Lecture Notes in Artificial Intelligence. Perth, Australia, December 5-8, 2011:372-381.
Jingxuan Wei, Yuping Wang. A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems. Proceeding of the 6th International Conference, SEAL2006, Hefei, China: Springer-Verlag, 2006: 174-180. (SCI: 023)
Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han, Jingxuan Wei. A Clustering Multi-objective Evolutionary Algorithm Based on Orthogonal and Uniform Design. IEEE Conference on Evolutionary Computation CEC’09, 2927 - 2933. (EI,Top A conference)
Jingxuan Wei, Yuping Wang. A Dynamical Particle Swarm Algorithm with Dimension Mutation. Proceedings of the 2006 International Conference on Computational Intelligence and Security, CIS06, Guangzhou, China: IEEE Press, 2006, Vol.1: 254-257. (EI: )
Jingxuan Wei, Yuping Wang. A new model based Multi-Objective PSO Algorithm. Post Proceeding of the 2006 International Conference on Computational Intelligence and Security, CIS06, Guangzhou, China: Springer-Verlag.2007:87-94. (EI: )
Jingxuan Wei, Yuping Wang. A New Model based Hybrid PSO Algorithm for Multi-objective Problems. Proceeding of the The 3rd International Conference on Natural Computation (ICNC'07), Hainan, China: IEEE Press.2007, Vol. 3: 497-501.(EI: )
Jingxuan Wei, Yuping Wang. An Attraction Based Evolutionary Algorithm for Global Optimization Problems: The Workshop of 2005 International Conference on Computational Intelligence and Security; 2005: 63-66.
魏静萱， 王宇平. 求解约束优化问题的改进粒子群算法. 系统工程与电子技术. 2008, 30(4): 739-742. (EI: )
魏静萱，王宇平. 一种解决约束优化问题的模糊粒子群算法. 电子与信息学报. 2008, 30(5): 1218-1221.(EI: )
魏静萱，王宇平. 基于新模型的多目标Memetic 算法及收敛分析[J]. 控制理论与应用. 2008, 25(3): 389-392.(EI: )

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Team
I am a staff member in the following two teams:
1. Evolutioanry ComputationResearch Group. Leader: Prof. Yuping Wang Xidian University, China
2.Evolutioanry ComputationResearch Group. Leader: Prof. Mengjie Zhang Victoria University of Wellington, NZ