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: 黑料传送门, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

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Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), 黑料传送门. 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

dc.title: Improved genetic algorithm using reinforcement learning to solve the re-entrant flexible flow shop scheduling problem dc.contributor.author: Wang, Xinzhuo; Liu, Chang; Wang, Rui; Yu, Zhenghao; Yang, Shengxiang dc.description.abstract: The Reentrant Flexible Flow Shop Scheduling Problem (RFFSP) involves multiple repetitions of job processing in the production system, which leads to higher scheduling complexity. To solve the RFFSP, this paper proposes a Reinforcement Learning- enhanced Genetic Algorithm (RL-GA). First, a model for the RFFSP is established with the goal of minimizing the total delay time. Then, based on the characteristics of the RFFSP, a machine selection and operation selection (MSOS) encoding scheme is designed. Finally, to address the issue of genetic algorithms getting stuck in local optima and having poor local search capabilities, a reinforcement learning-based method for selecting crossover and mutation strategies is proposed. This allows the algorithm to adaptively choose more suitable crossover and mutation strategies during the iteration process. The proposed method is validated by comparing it with five other scheduling algorithms, demonstrating an improvement in the algorithm's performance.
dc.title: Feasible regions identification based on historical solutions for constrained optimization problems dc.contributor.author: Shan, Mengli; Li, Changhe; Liu, Xiaobo; Peng, Mai; Mavrovouniotis, Michalis; Yang, Shengxiang dc.description.abstract: The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into 蔚-feasible and 蔚-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.
dc.title: Dynamic multi-objective optimisation based on vector autoregressive evolution dc.contributor.author: Jiang, Shouyong; Wang, Yong; Hu, Yaru; Zhang, Qingyang; Yang, Shengxiang dc.description.abstract: Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation (EAH) to address environmental changes in DMO. In light of mutual dependency between decision variables in Pareto-optimal solutions, VARE builds an efficient VAR model, capturing such mutual relationship while handling dense model parameterisation with dimensionality reduction, to predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity, for scenarios where predictive approaches are unsuitable, by making hypermutation aware of the significance of environmental changes in both decision and objective spaces. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a variety of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive empirical studies. Specially, the proposed algorithm is computationally much faster than popular transfer learning based approaches while producing significantly better results. dc.description: 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.
dc.title: Nearest-better network for visualizing and analyzing combinatorial optimization problems: A potential unified tool dc.contributor.author: Diao, Yiya; Li, Changhe; Zeng, Sanyou; Cai, Xinye; Yang, Shengxiang; Coello Coello, Carlos A. dc.description.abstract: The Nearest-Better Network (NBN) is a powerful method to visualize sampled data for continuous optimization problems while preserving multiple landscape features. However, the calculation of NBN is very time-consuming, and the extension of the method to combinatorial optimization problems is challenging but very important for analyzing the algorithm's behavior. This paper provides a straightforward theoretical derivation showing that the NBN network essentially functions as the maximum probability transition network for algorithms. This paper also presents an efficient NBN computation method with logarithmic linear time complexity to address the time-consuming issue. By applying this efficient NBN algorithm to the OneMax problem and the Traveling Salesman Problem (TSP), we have made several remarkable discoveries for the first time: The fitness landscape of OneMax exhibits neutrality, ruggedness, and modality features. The primary challenges of TSP problems are ruggedness, modality, and deception. Three state-of-the-art TSP algorithms (EAX, LKH, and NLKH) have limitations when addressing challenges related to modality and deception, respectively. LKH, based on local search operators, fails when there are deceptive solutions near global optima. EAX, which is based on a single population, can efficiently maintain diversity. However, when multiple attraction basins exist, EAX retains individuals within multiple basins simultaneously, reducing inter-basin interaction efficiency and leading to algorithm's stagnation. NLKH improves over LKH by leveraging learned edge weights to increase the chance of reaching the global basin, but it remains vulnerable to deceptive funnels due to biased learning from underrepresented complex instances. dc.description: 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.
dc.title: A coevolutionary response framework for dynamic constrained multi-objective optimization problems dc.contributor.author: Bao, Qian; Wang, Maocai; Yang, Shengxiang; Dai, Guangming; Chen, Xiaoyu dc.description.abstract: Dynamic constrained multi-objective optimization problems (DCMOPs) present significant challenges due to the evolving nature of both objectives and constraints. These problems require optimization algorithms that can efficiently adapt to dynamic environments while maintaining a balance between convergence and diversity. To address these challenges, we propose a novel cooperative response dynamic constrained multi-objective optimization (CRDCMO) framework. The framework introduces two key strategies: (1) population reinitialization guided by historical environmental information, tailored to different types of environmental changes, and (2) dynamic adjustment of auxiliary population tasks, optimizing resource allocation with a focus on tracking the constrained Pareto-optimal front (CPF). These strategies enhance the algorithm鈥檚 adaptability to environmental changes and improve CPF tracking efficiency. The CRDCMO framework is extensively evaluated on several benchmark test suites, as well as a real-world energy optimization problem. Experimental results demonstrate that CRDCMO outperforms seven state-of-the-art algorithms, underscoring its effectiveness and robustness in dynamic environments. This framework not only provides a comprehensive solution for DCMOPs but also contributes to the advancement of dynamic optimization algorithms. dc.description: 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.
dc.title: Solving dynamic multi-objective engineering design problems via fuzzy c-means prediction algorithm dc.contributor.author: Zhang, Qingyang; Fu, Xueliang; Yang, Shengxiang; Jiang, Shouyong; Li, Miqing; Zheng, Zedong dc.description.abstract: This paper proposes a new prediction algorithm by integrating the fuzzy c-means and regression analysis fitting techniques with multi-objective differential evolution (FRMODE) to solve dynamic multi-objective optimization problems. When environmental changes are detected, the main purpose of FRMODE is to predict high-quality populations that can effectively track the moving Pareto-optimal set. Specifically, the fuzzy c-means (FCM) algorithm clusters the populations obtained from the past two adjacent environments. The center points of populations are utilized to define the moving direction, which is used to predict high-quality agents based on previous non-dominated individuals. Then, linear and non-linear regression analysis fitting strategies are developed to model the distribution of variables according to the variables鈥� characteristics. Besides that, the partial mutation strategy is also utilized to guide individuals toward more promising regions by intensifying the search around current agents. To evaluate the performance of the proposed algorithm, experiments are conducted on a set of benchmark functions with various dynamic difficulties, as well as on two classical dynamic engineering design problems. The experimental results demonstrate that FRMODE is more competitive compared with several state-of-the-art algorithms. dc.description: 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.
dc.title: Competitive many-task differential evolution with reinforcement learning and meta-knowledge transfer dc.contributor.author: Song, Yuxuan; Xu, Yue; Pi, Dechang; Yang, Shengxiang dc.description.abstract: Competitive many-task optimization (CMaTO) is a special many-task optimization paradigm whose purpose is to find the best optimal solution for all tasks. However, the existing CMaTO algorithms perform poorly in the design of knowledge transfer from auxiliary tasks to the main task, resulting in a prolonged period of stagnant optimal fitness for the main task. To address these shortcomings, a competitive many-task optimization algorithm is proposed, based on reinforcement learning and meta-knowledge transfer, leveraging differential evolution as a foundational evolutionary strategy. This algorithm employs a reinforcement learning algorithm to select auxiliary tasks that can accelerate the convergence of the optimal value or jump out of the stagnation state according to the evolutionary state. Meanwhile, a stagnation detection operator is designed to switch the main task when the optimal value stagnation threshold upper limitation is reached. Furthermore, the meta-knowledge migration algorithm is embedded to judge the evolutionary state of the population based on the distance between the optimal solution and the centroid of the population. The migration radius is adaptively adjusted, and the knowledge is utilized to facilitate the evolution of high-quality solutions for the source task, which can assist the population in accelerating convergence or escaping a local optimum. To evaluate the performance of the proposed algorithm, three CMaTO benchmark test suites and a real-world Unmanned Aerial Vehicle (UAV) task allocation problem are chosen to compare it with other state-of-the-art strategies. The results show that the proposed algorithm achieved better performance. dc.description: 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.
dc.title: An improved adaptive large neighborhood search for the home health care routing and scheduling problem with multiple mixed time windows dc.contributor.author: Zhang, Wei; Ma, Wen; Yang, Shengxiang; Chen, Shengzong; Zhang, Jihui dc.description.abstract: The growing challenges posed by population aging and urbanization have intensified the need for efficient home health care (HHC) services to alleviate the great pressure on healthcare resources. This study addresses the Home Health Care Routing and Scheduling Problem (HHCRSP), which involves optimizing caregivers鈥� daily schedules while considering complex real-world constraints, including skill matching, mixed hard and soft time windows, synchronized services, and workload balancing. To address these challenges, a novel mixed-integer linear programming (MILP) model and an improved adaptive large neighbourhood search (IALNS) algorithm are proposed. The algorithm integrates an elite archive mechanism and introduces new removal and insertion operators, thus maintains archive diversity, and effectively explores the solution space through reconstruction, crossover, and mutation. Furthermore, it adopts a two-stage approach to ensure solution feasibility. Extensive computational experiments show the effectiveness of the proposed method and the competitiveness of the IALNS. Also, we examine the impact of the proportion of clients purchasing on-time services, time window penalty coefficients, and the number of available time windows on scheduling solutions. These findings underscore the proposed algorithm鈥檚 potential to reduce the cost of HHC services. dc.description: 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.
dc.title: Knowledge transfer with mixture model in dynamic multi-objective optimization dc.contributor.author: Zou, Juan; Hou, Zhanglu; Jiang, Shouyong; Yang, Shengxiang; Ruan, Gan; Xia, Yizhang; Liu, Yuan dc.description.abstract: Most existing dynamic multi-objective evolutionary algorithms (DMOEAs) have been designed to handle dynamic multi-objective optimization problems (DMOPs) with regular environmental changes. However, they often overlook scenarios where environmental changes are irregular and less predictable. Recently, knowledge transfer has been proposed as a novel paradigm for solving DMOPs. Despite this, most transfer strategies only consider transferring knowledge obtained from the previous environment while ignoring significant differences that may exist between adjacent environments due to irregular changes. To address these issues, this paper proposes a novel knowledge transfer strategy based on a Gaussian mixture model (denoted as KTMM) for solving DMOPs with irregular changes. In particular, an adaptive Gaussian mixture model is designed to capture the knowledge of historical environments, which is then transferred to generate an initial population for the new environment. Additionally, a new method for controlling irregular changes is introduced into widely-used benchmarks to form the DMOP benchmark with irregular changes. Our proposed KTMM is compared with six state-of-the-art DMOEAs on several benchmark problems with irregular changes. Experimental results demonstrate the superiority of our proposed method in most test instances and in a real-world problem. dc.description: 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.
dc.title: A decomposition framework with dual populations and dual stages for constrained multi-objective optimization dc.contributor.author: Hou, Zhanglu; Ye, Jialu; Xia, Yizhang; Gong, Yibin; Zou, Juan; Yang, Shengxiang dc.description.abstract: The main challenge of constrained multi-objective optimization involves achieving a good balance among feasibility, convergence, and diversity simultaneously. However, most existing methods exhibit an imbalance, making it hard to converge towards Pareto-optimal front (PF) while maintaining the diversity of feasible solutions. To address this issue, this paper proposes a new dual-population and dual-stage constrained multi-objective optimization algorithm, denoted as DD-M2M, based on the multi-objective-to-multi-objective decomposition framework (M2M). In this approach, dual populations, referred to as CP and DP, are employed collaboratively. More specifically, in the first stage, the DP evolves independently without considering constraints, focusing solely on convergence towards the unconstrained PF, while the CP evolves with a weak collaboration with the DP, driven by feasibility-based environment selection rules. In the second stage, the dual populations are both divided into multiple sub-populations within the M2M framework, generating offspring in a strongly collaborative manner to eventually converge to the constrained PF while ensuring solution diversity. Experimental results on two widely-used test suites fully demonstrate the superiority of DD-M2M compared to seven state-of-the-art methods on most test problems. Additionally, the proposed method is applied to real-world problems, and experimental results confirm its effectiveness in addressing practical challenges. dc.description: 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.

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)

黑料传送门 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 (), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (), 2012–2018.
Chair, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (), 2011–2018.
Senior Member, , since 2014.
Member, , 2000 – 2013.
Member, IEEE Computational Intelligence Society (), since 2005.
Member, Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (), since 2011.
Member, Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (), since 2013.
Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (), 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 黑料传送门, started in October 2017.
William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at 黑料传送门, 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
, 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 黑料传送门 and , 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

黑料传送门 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.
黑料传送门 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.
黑料传送门 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
黑料传送门 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
黑料传送门 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
黑料传送门 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

黑料传送门 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

Associate Editor (January 2015-now), , Elsevier, UK
Associate Editor (January 2015-now), , Taylor and Francis Group, UK
Associate Editor (October 2014-now), , IEEE Press, USA
Associate Editor (2016-2017), , Elsevier, UK
Member of Editorial Board (August 2014-now), , Springer, Germany

Member of editorial board (2012-now), , MIT Press, USA
Member of editorial board (2007-present), International Journal of Computational Science, Global Information Publisher (GIP), Hong Kong
Area editor (2006-present), , World Academic Press, World Academic Union, UK
Associate editor (2006-August 2008), Journal of Artificial Evolution and Applications, Hindawi Publishing Corporation, USA
Member of editorial board (2009-2010), , IN-TECH Education and Publishing, Austria
Guest-editor, Thematic Issue on Memetic Computing in the Presence of Uncertainties, , Vol. 2, No. 2, June 2010, Springer
Guest-editor, Special Issue on Evolutionary Computation in Dynamic and Uncertain Environments, , Vol. 7, No. 4, December 2006, Springer