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Professor Shengxiang Yang

Job: Professor of Computational Intelligence, Director of the Centre for Computational Intelligence (CCI)

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

W: http://www.tech.dmu.ac.uk/~syang/

 

Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), De Montfort University. Before joining the CCI in July 2012, he worked at Brunel University, University of Leicester, and King's College London as a Senior Lecturer, Lecturer, and Post-doctoral Research Associate, respectively.

Shengxiang's main research interests lie in evolutionary computation. He is particularly active in the area of evolutionary computation in dynamic and uncertain environments. Shengxiang has also published on the application of evolutionary computation in communication networks, logistics, transportation systems, and manufacturing systems, etc.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs

  • Learning to search promising regions by a Monte-Carlo tree model
    Learning to search promising regions by a Monte-Carlo tree model Xia, Hai; Li, Changhe; Zeng, Sanyou; Tan, Qingshan; Wang, Junchen; Yang, Shengxiang In complex optimization problems, learning where to search is a difficult but critical decision for all search algorithms. Evolutionary computation methods also encounter a dilemma about where to explore or exploit. In this paper, a Monte-Carlo tree is constructed to guide evolutionary algorithms to search multiple promising regions simultaneously. In the Monte-Carlo tree model, a root node that contains all historical solutions represents the whole solution space. In each node of the tree, with k-means clustering method to partition solutions into different groups, group labels of the solutions are used to train support vector regression, which can learn a boundary to partition a region into different sub-regions. According to state values of nodes, reproduction operators of evolutionary algorithms are strengthened by selecting solutions in the most promising regions. From experimental results on multimodal problems, the proposed algorithm shows a competitive performance, which also indicates a great potential for applications to other kinds of optimization problems.
  • A multiobjective evolutionary algorithm based on decision variable classification for many-objective optimization
    A multiobjective evolutionary algorithm based on decision variable classification for many-objective optimization Liu, Qiuyue; Zou, Juan; Yang, Shengxiang; Zheng, Jinhua Multiobjective evolutionary algorithms (MOEAs) have faced the challenge of balancing diversity and convergence in dealing with many-objective optimization problems (MaOPs). Most of them use a series of strategies to increase the selection pressure among solutions for convergence promotion, or additional auxiliary strategies for diversity maintenance. Decision variable classification (DVC), as the method that analyzes the feature of an MaOP, can help MOEAs search for optimal solutions in terms of convergence and diversity through optimizing the corresponding category of decision variables. Therefore in this paper, we propose a new DVC method by analyzing the monotonicities of objectives. Unlike other DVC methods, it does not need to consider dominance relationships or help with extra vectors. Based on the classification results, we design a new directional crossover (DC) method for generating promising solutions. This crossover method has a higher probability that the generated offspring can integrate the advantages of the parents in convergence and diversity. Incorporating it with MOEA, a DVC-based MOEA (DVC-MOEA) is proposed for dealing with MaOPs. In DVC-MOEA, two archives focusing on convergence and diversity separately are maintained. In addition, an interval mapping(IM) strategy is designed to obtain solutions with good diversity, especially for some problems with biased features. To evaluate the performance of DVC-MOEA on MaOPs, comparison experiments are conducted on two wide used benchmarks with nine state-of-the-art MOEAs. The experimental results show that DVC-MOEA has high competitiveness over these MOEAs in dealing with MaOPs. Moreover, three variants are compared with DVC-MOEA respectively, and the comparison experimental results confirm the effect of the three strategies (DVC, DC, and IM) in our proposed algorithm. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables
    Cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables Xu, Biao; Gong, Dunwei; Zhang, Yong; Yang, Shengxiang; Wang, Ling; Fan, Zhun; Zhang, Yonggang Multi-objective optimization problems (MOPs) with changing decision variables exist in the actual industrial production and daily life, which have changing Pareto sets and complex relations among decision variables and are difficult to solve. In this study, we present a cooperative co-evolutionary algorithm by dynamically grouping decision variables to effectively tackle MOPs with changing decision variables. In the presented algorithm, decision variables are grouped into a series of groups using maximum entropic epistasis (MEE) at first, with decision variables in different groups owning a weak dependency. Subsequently, a sub-population is generated to solve decision variables in each group with an existing multi-objective evolutionary algorithm (MOEA). Further, a complete solution including all the decision variables is achieved through the cooperation among sub-populations. Finally, when a decision variable is added or deleted from the existing problem, the grouping of decision variables is dynamically adjusted based on the correlation between the changed decision variable and existing groups. To verify the performance of the developed method, the presented method is compared with five popular methods by tackling eight benchmark optimization problems. The experimental results reveal that the presented method is superior in terms of diversity, convergence, and spread of solutions on most benchmark optimization problems. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A level-based multi-strategy learning swarm optimizer for large-scale multi-objective optimization
    A level-based multi-strategy learning swarm optimizer for large-scale multi-objective optimization Qi, Sheng; Zou, Juan; Yang, Shengxiang; Zheng, Jinhua The continuous roaming of particles in high-dimensional space makes it difficult for particle swarm optimization to achieve better optimization results. On the other hand, increasing the dimensionality may also bring about an explosive increase in the number of locally optimal solutions surrounded by more significant local optimal regions. Therefore, the algorithm requires high convergence while maintaining good diversity. This paper proposes a level-based multi-strategy learning swarm algorithm called LSLSO. LSLSO’s optimization process is divided into the level-based multi-strategies search and the detailed search stages. First, particles are divided into four levels according to their fitness value. When the particles are at different levels, the particles have different learning strategies to update. Particles with better fitness focus on exploiting space. In contrast, particles with poor fitness will focus on exploring space. In the detailed search stage, particles at the same level learn from each other. Particles with similar fitness to detail search for the small gaps in the promising space already explored. The theoretical discussion results show that LSLSO has strong competitiveness in exploration and exploitation capabilities. Moreover, it shows good performance compared with the most advanced large-scale multi-objective optimization algorithms on the LSMOP and LMF problems. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • HINO: An optimization algorithm for energy minimization of UAV-assisted mobile edge computing
    HINO: An optimization algorithm for energy minimization of UAV-assisted mobile edge computing Chen, Yang; Pi, Dechang; Yang, Shengxiang; Xu, Yue; Chen, Junfu; Mohamed, Ali Wagdy Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Dealing with distribution mismatch in semi-supervised deep learning for Covid-19 detection using chest X-ray images
    Dealing with distribution mismatch in semi-supervised deep learning for Covid-19 detection using chest X-ray images Calderon-Ramirez, Saul; Yang, Shengxiang; Moemeni, Armaghan; Elizondo, David In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model’s accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32%, when compared to the previous state of the art methods. The good results yielded by our method leads us to argue in favour for a more data-centric approach to improve model’s accuracy. Furthermore, the developed method can be used to measure data effectiveness for semi-supervised deep learning model training. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Dataset similarity to assess semi-supervised learning under distribution mismatch between the labelled and unlabelled datasets
    Dataset similarity to assess semi-supervised learning under distribution mismatch between the labelled and unlabelled datasets Calderon-Ramirez, Saul; Oala, Luis; Torrents-Barrena, Jordina; Yang, Shengxiang; Elizondo, David; Moemeni, Armaghan; Colreavy-Donnelly, Simon; Samek, Wojciech; Molina-Cabello, Miguel; Lopez-Rubio, Ezequiel Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for machine learning when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying degrees of distribution mismatch regarding the labelled datasets. It begs the question which unlabelled dataset to choose for good SSDL outcomes. ftentimes, semantic heuristics are used to match unlabelled data with labelled data. However, a quantitative and systematic approach to this election problem would be preferable. In this work, we first test the SSDL MixMatch algorithm under various distribution mismatch configurations to study the impact on SSDL accuracy. Then, we propose a quantitative unlabelled dataset selection heuristic based on dataset dissimilarity measures. These are designed to systematically assess how distribution mismatch between the labelled and unlabelled datasets affects MixMatch performance. We refer to our proposed method as deep dataset dissimilarity measures (DeDiMs), designed to compare labelled and unlabelled datasets. They use the feature space of a generic Wide-ResNet, can be applied prior to learning, are quick to evaluate and model agnostic. The strong correlation in our tests between MixMatch accuracy and the proposed DeDiMs suggests that this approach can be a good fit for quantitatively ranking different unlabelled datasets prior to SSDL training. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica
    A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica Calderon-Ramirez, Saul; Murillo-Hernandez, Diego; Rojas-Salazar, Kevin; Elizondo, David; Yang, Shengxiang; Moemeni, Armaghan; Molina-Cabello, Miguel The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labelled images, which can be expensive to obtain as time and effort from clinical practitioners are required. To address this, a number of publicly available datasets have been built with data from different hospitals and clinics, which can be used to pre-train the model. However, using models trained on these datasets for later transfer learning and model fine-tuning with images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. In this work, a real-world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. A common approach to further improve the model’s performance under such small labelled target dataset setting is data augmentation. However, often cheaper unlabelled data is available from the target clinic. Therefore, semi-supervised deep learning, which leverages both labelled and unlabelled data, can be used in such conditions. In this work, we evaluate the semi-supervised deep learning approach known as MixMatch, to take advantage of unlabelled data from the target dataset, for whole mammogram image classification. We compare the usage of semi-supervised learning on its own, and combined with transfer learning (from a source mammogram dataset) with data augmentation, as also against regular supervised learning with transfer learning and data augmentation from source datasets. It is shown that the use of a semi-supervised deep learning combined with transfer learning and data augmentation can provide a meaningful advantage when using scarce labelled observations. Also, we found a strong influence of the source dataset, which suggests a more data-centric approach needed to tackle the challenge of scarcely labelled data. We used several different metrics to assess the performance gain of using semi-supervised learning, when dealing with very imbalanced test datasets (such as the G-mean and the F2-score), as mammogram datasets are often very imbalanced. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Evolutionary dynamic multi-objective optimization: A survey
    Evolutionary dynamic multi-objective optimization: A survey Jiang, Shouyong; Zou, Juan; Yang, Shengxiang; Yao, Xin Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young area of investigation that is rapidly growing. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of effect, EDMO has achieved significant advancements on various topics, including dynamics characterisation, change detection, change response, performance assessment. In addition, there have been a number of studies on application of EDMO to real-world problems. This paper presents a broad survey and taxonomy of existing research on EDMO. As a result, multiple future research directions are highlighted to further promote the development of the EDMO research field. The file attached to this record is the author's final peer reviewed version.
  • A multiobjective state transition algorithm based on decomposition
    A multiobjective state transition algorithm based on decomposition Zhou, Xiaojun; Gao, Yuan; Yang, Shengxiang; Yang, Chunhua; Zhou, Jiajia Aggregation functions largely determine the convergence and diversity performance of multi-objective algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. To deal with this issue, a new multiobjective state transition algorithm based on modified decomposition method (MOSTA/D) is proposed. According to the analysis of the relationship between the weight vectors and candidate solutions under the Tchebycheff decomposition scheme, the concept of matching degree is introduced which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed in MOSTA/D. It can adaptively select the candidate solutions which are better matched with the weight vectors. This proposed MOSTA/D decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner. Each individual solution in the population of MOSTA/D is associated with a subproblem. Four mutation operators in STA are adopted to generating candidate solutions on subproblems and maintaining the population diversity. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state-of-the-art evolutionary algorithms on tackling a set of benchmark problems with complicated Pareto fronts and a typical engineering optimization problem. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Click here to view a full listing of Shengxiang Yang's publications and outputs.

Research interests/expertise

  • Evolutionary Computation

  • Swarm Intelligence

  • Meta-heuristics

  • Dynamic Optimisation Problems

  • Multi-objective Optimisation Problems

  • Relevant Real-World Applications

Areas of teaching

Research Methods for Intelligent Systems and Robotics MSc, Software Engineering MSc, Computing MSc, and Business Intelligence Systems and Data Mining MSc Degrees.

Qualifications

BSc in Automatic Control, Northeastern University, China (1993)

MSc in Automatic Control, Northeastern University, China (1996)

PhD in Systems Engineering Northeastern University, China (1999)

Courses taught

I have taught numerous modules at both undergraduate and postgraduate level. Quite a number of modules I taught were significantly developed by myself. The modules I taught are usually designed to be practice-oriented with problem-solving lab sessions based on Java or C++ programming, and hence are highly interesting to and greatly useful for students. They are also very important for different degree programmes in Computer Science and relevant subjects. Some of the modules I have taught are listed as follows:

  • CS3002 Artificial Intelligence (2010 – 2012, Brunel University): 3rd year Computer Science (Artificial Intelligence) BSc module, module leader

  • CS2005 Networks and Operating Systems (2010 – 2012, Brunel University): 2nd year Network Computing BSc module, part module

  • CS5518 Business Integration (2011-2012, Brunel University): Business Systems Integration MSc module, part module

  • CO2017 Networks and Distributed Systems (2005–2010, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO2005 Object-Oriented Programming Using C++ (2006–2009, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO1003 Program Design (2006-2007, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO3097 Programming Secure and Distributed Systems (2003–2005, University of Leicester): 3rd year Computer Science BSc & Advanced Computer Science MSc module, module leader

  • CO1017 Operating Systems and Networks (2001 – 2004, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO1016 Computer Systems (2000 – 2002, University of Leicester): 1st year Computer Science BSc module, part module

I have also co-ordinated several BSc projects, as shown below.

  • CS3072/CS3074/CS3105/CS3109 BSc Final Year Projects (2010 – 2012, Brunel University): Co-ordination Team Member

  • CO3012/CO3013/CO3015 Computer Science BSc Final Year Projects (2004 – 2010, University of Leicester): Co-ordinator

  • CO3120 Computer Science with Management BSc Final Year Project (2007 – 2010, University of Leicester): Co-ordinator

  • CO3014 Mathematics and Computer Science BSc Final Year Project (2004 – 2010, University of Leicester): Co-ordinator

  • CO2015 Second Year BSc Software Engineering Project (2003 – 2004, University of Leicester): Co-ordinator

Honours and awards

  • Nominatee to the Best Paper Award for EvoApplications 2016: Applications of Evolutionary Computation, for the paper "Direct memory schemes for population-based incremental learning in cyclically changing environments" by Michalis Mavrovouniotis and Shengxiang Yang, published in EvoApplications 2016: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9598, pp. 233-247, 2016.

  • Nominatee for the Best-Paper Award of the ACO-SI Track at the 2015 Genetic and Evolutionary Computation Conference, for the paper "An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem" by Michalis Mavrovouniotis, Felipe Martins Muller and Shengxiang Yang, published in the Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 49-56, 2015.

  • Winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award, for the paper entitled "A test problem for visual investigation of high-dimensional multi-objective search" by Miqing Li, Shengxiang Yang and Xiaohui Liu, published in the Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 2140-2147, 2014.

  • Nominatee for the 2005 Genetic and Evolutionary Computation Conference Best Paper Award, for the paper "Memory-based immigrants for genetic algorithms in dynamic environments" by Shengxiang Yang, published in the Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005.

  • Visiting Professor (2012 – 2014, 2016-2018), College of Information Engineering, Xiangtan University, China

  • Visiting Professor (2011 – 2017), College of Mathematics and Statistics, Nanjing University of Information Science and Technology, China

Membership of professional associations and societies

  • Founding Chair, Task Force on Intelligent Network Systems (TF-INS), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), 2012–2018.

  • Chair, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2011–2018.

  • Senior Member, IEEE, since 2014.

  • Member, IEEE, 2000 – 2013.

  • Member, IEEE Computational Intelligence Society (IEEE CIS), since 2005.

  • Member, Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), since 2011.

  • Member, Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), since 2013.

  • Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2003 – 2010.

Current research students

First Supervisor:

  • Muhanad Tahrir Younis: Swarm intelligence for dynamic job scheduling in grid computing, started from October 2014

  • Conor Fahy: Evolutionary computation for data stream analysis, started from October 2015

  • Zedong Zheng: started from October 2016
  • Matthew Fox: started from October 2017

Second Supervisor:

  • Ahad Arshad: PhD candidate, co-supervised with Prof. Paul Fleming at De Montfort University, started in October 2017.
  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at De Montfort University, started in April 2012

Complete PhD Students (I was the 1st Supervisor):

  • Changhe Li: Particle swarm optimisation in stationary and dynamic environments, 2011

  • Imtiaz Ali Korejo: Adaptive mutation operators for evolutionary algorithms, 2011

  • Sadaf Naseem Jat: Genetic algorithms for university course timetabling problems, 2012

  • Shakeel Arshad: Sequence based memetic algorithms for static and dynamic travelling salesman problems, 2012

  • Michalis Mavrovouniotis: Ant Colony Optimization in Stationary and Dynamic Environments, 2013

  •  Miqing Li: Evolutionary Many-Objective Optimization: Pushing the Boundaries, 2015
  • Jayne Eaton: Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems, 2017
  • Shouyong Jiang: Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization, 2017

Externally funded research grants information

  • EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships (PI, Project ID: 661327, 09/2015-08/2017, €195,455): Evolutionary Computation for Dynamic Constrained Optimization Problems (ECDCOP)
  • EPSRC (PI, Standard Research Project, EP/K001310/1, 18/2/2013-17/02/2017, £445,069): Evolutionary Computation for Dynamic Optimisation in Network Environments

  • EPSRC (PI, Standard Research Project, EP/E060722/1 and EP/E060722/2, 1/1/2008-1/7/2011, £307,469): Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications

  • EPSRC (PI, Overseas Travel Grants GR/S79718/01, 1/11/2003-31/1/2004, £6,700): Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing. This grant supported my research visit to Waseda University, Japan, during my Sabbatical leave period. Additionally, Waseda University, Japan contributed JPY140,000 (~£800) toward the visit

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2012-31/12/2013, CNY300,000 (~£30,000)): Evolutionary Computation for Dynamic Scheduling Problems in Process Industries

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2010-31/12/2011, CNY150,000 (~£15,000)): Evolutionary Computation for Dynamic Optimization and Scheduling Problems

  • Transport iNet, European Regional Development Fund (Co-I, 11/11/2013 - 28/02/2015, £62,134), Evolutionary Computation for Optimised Rail Travel (EsCORT). This is a linked project between De Montfort University and Go Travel Solutions, a Leicester based SME specialising in assisting businesses to develop sustainable travel solutions, covering people and goods.
  • Hong Kong Polytechnic University Research Grants (Co-I, Grant G-YH60, 1/7/2009-30/6/2010, HKD120,000 (~£10,000)): Improved Evolutionary Algorithms with Primal-Dual Population for Dynamic Variation in Production Systems. Partners:

In addition, I have also received several conference travel grants from UK Research Councils, e.g., Royal Society Conference Travel Grant (£700 in 2007 and £719 in 2005) and Royal Academy of Engineering Conference Grant (£800 in 2007 and £1,200 in 2006).

Internally funded research project information

  • De Montfort University Higher Education Innovation Fund (HEIF) 2017-18 (Co-I, 01/12/2017-31/07/2018, £14,000): Brian-Computer-Interface Prototyping System: Data-based Filtering and Dynamic Characterisation.
  • De Montfort University Higher Education Innovation Fund (HEIF) 2015-16 (PI, 01/01/2016-31/07/2016, £24,800): Development of a Dynamic Resource Scheduling Prototype System for Airports.

  • De Montfort University PhD Studentships 2017-18 (PI, 1/10/2017–30/09/2020, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University Fee Waiver PhD Scholarships 2016-17 (PI, 1/10/2016–30/09/2019, approximately £40,000): supporting fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2015-16 (PI, 1/10/2015–30/09/2018, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/10/2013–30/09/2016, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/4/2013–31/03/2016, approximately £60,000): supporting stipend and fees for one home PhD student for three years

  • Brunel University PhD Studentships 2011-12 (PI, 01/10/2011–30/09/2014, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • University of Leicester PhD Studentships 2008-09 (PI, 1/10/2008–30/9/2011, approximately £50,000): supporting stipend and fees for one PhD student for three years

  • University of Leicester Research Fund 2001 (PI, 1/1/2001- 31/12/2001, £3,200): Using Neural Network and Genetic Algorithm Methods for Job-Shop Scheduling Problem.

Professional esteem indicators

Shengxiang-Yang