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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: 51Âþ»­, 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), 51Âþ»­. 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: Nearest-better network assisted fitness landscape analysis of contaminant source identification in water distribution network dc.contributor.author: Diao, Yiya; Li, Changhe; Zeng, Snayou; Yang, Shengxiang dc.description.abstract: Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem’s landscape features, which has limited their effectiveness in practice. Despite this, there has been little experimental analysis of the fitness landscape for CSWIDN, particularly given its mixed-encoding nature. This study addresses this gap by conducting a comprehensive fitness landscape analysis of CSWIDN using the Nearest-Better Network (NBN), the only applicable method for mixed-encoding problems. Our analysis reveals for the first time that CSWIDN exhibits the landscape features, including neutrality, ruggedness, modality, dynamic change, and separability. These findings not only deepen our understanding of the problem’s inherent landscape features but also provide quantitative insights into how these features influence algorithm performance. Additionally, based on these insights, we propose specific algorithm design recommendations that are better suited to the unique challenges of the CSWIDN problem. This work advances the knowledge of CSWIDN optimization by both qualitatively characterizing its landscape and quantitatively linking these features to algorithms’ behaviors. dc.description: open access article

  • dc.title: Benchmark Problems for IEEE WCCI2024 Competition on Dynamic Constrained Multiobjective Optimization dc.contributor.author: Guo, Yinan; Chen, Guoyu; Yue, Caitong; Liang, Jing; Wang, Yong; Yang, Shengxiang

  • dc.title: Benchmark Problems for CEC2023 Competition on Dynamic Constrained Multiobjective Optimization dc.contributor.author: Gui, Yinan; Chen, Guoyu; Yue, Caitong; Liang, Jing; Wang, Yong; Yang, Shengxiang

  • dc.title: Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB) dc.contributor.author: Yazdani, Danial; Mavrovouniotis, Michalis; Li, Changhe; Luo, Wenjian; Omidvar, Mohammad Nabi; Gandomi, Amir H.; Nguyen, Trung Thanh; Branke, Juergen; Li, Xiaodong; Yang, Shengxiang; Yao, Xin dc.description.abstract: This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes with a broad spectrum of characteristics, offering everything from unimodal to highly multimodal landscapes and ranging from symmetric to highly asymmetric configurations. The landscapes also vary in texture, from smooth to highly irregular surfaces, encompassing diverse degrees of variable interaction and conditioning. This document delves into the intricacies of GMPB, detailing the myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics. GMPB's MATLAB implementation is available on the EDOLAB Platform.

  • dc.title: IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark dc.contributor.author: Yazdani, Danial; Branke, Juergen; Omidvar, Mohammad Nabi; Li, Xiaodong; Li, Changhe; Mavrovouniotis, Michalis; Nguyen, Trung Thanh; Yang, Shengxiang; Yao, Xin dc.description.abstract: This document describes the Generalized Moving Peaks Benchmark (GMPB) that generates continuous dynamic optimization problem instances. The landscapes generated by GMPB are constructed by assembling several components with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning. In this document, we explain how these characteristics can be generated by different parameter settings of GMPB. The MATLAB source code of GMPB is also explained.

  • dc.title: Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization dc.contributor.author: Luo, Wenjian; Xu, Peilan; Yang, Shengxiang; Shi, Yuhui dc.description.abstract: The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark.

  • dc.title: Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments dc.contributor.author: Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui dc.description.abstract: Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolu tionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.

  • dc.title: A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization dc.contributor.author: Deng, Qi; Liu, Yuan; Yang, Shengxiang; Zou, Juan; Li, Xijun; Xia, Yizhang; Zheng, Jinhua dc.description.abstract: Multimodal multiobjective optimization problems (MMOPs) are a challenging class of problems. Several advanced evolutionary algorithms have been developed to solve MMOPs, but these algorithms still have some limitations, such as having many parameters, slow convergence speed, and unsatisfactory performance. To solve these problems, we propose a repulsive-distance-based maximum diversity selection algorithm (RMDS) which aims, during environmental selection, to select individuals with the best comprehensive diversity through the repulsive distance. The repulsive distance is the comprehensive Euclidean distance between an individual and selected individuals with the consideration of distribution of individuals in both the decision and objective spaces. RMDS has the following advantages: first, the repulsive distance allows rational selection of well-distributed solutions in the non-parameterized case. Second, the repulsive distance acts both in the decision and objective spaces, so it provides a good balance between the distribution of the solution set in these two spaces. Third, RDMS has a straightforward principle. Experimental results show that RMDS has superior performance incomparison with other well-known multimodal multiobjective evolutionary algorithms on 31 test functions. 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 prediction and weak coevolution-based dynamic constrained multi-objective optimization dc.contributor.author: Gong, Dunwei; Rong, Miao; Hu, Na; Wang, Yan; Pedrycz, Witold; Yang, Shengxiang dc.description.abstract: Dynamic multi-objective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with dynamic multi-objective optimization problems (DMOPs). However, existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this paper, we propose a prediction and weak coevolutionary multi-objective optimization algorithm (PWDCMO) to handle dynamic constrained multi-objective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multi-objective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with four popular dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO. 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 algorithm with detection and supervision strategy for constrained multiobjective optimization dc.contributor.author: Feng, Jian; Liu, Shaoning; Yang, Shengxiang; Zheng, Jun; Xiao, Qi dc.description.abstract: Balancing objectives and constraints is challenging in addressing constrained multiobjective optimization problems (CMOPs). Existing methods may have limitations in handling various CMOPs due to the complex geometries of the Pareto front (PF). And the complexity arises from the constraints that narrow the feasible region. Categorizing problems based on their geometric characteristics facilitates facing this challenge. For this purpose, this article proposes a novel constrained multiobjective optimization framework with detection and supervision phases, called COEA-DAS. The framework categorizes the problems into four types based on the overlap between the obtained approximate unconstrained PF and constrained PF to guide the coevolution of the two populations. In the detection phase, the detection population approaches the unconstrained PF ignoring the constraints. The main population is guided by the detection population to cross infeasible barriers and approximate the constrained PF. In the supervision phase, specialized evolutionary mechanisms are designed for each possible problem type. The detection population maintains evolution to assist the main population in spreading along the constrained PF. Meanwhile, the supervision strategy is conducted to reevaluate the problem types based on the evolutionary state of the populations. This idea of balancing constraints and objectives based on the type of problem provides a novel approach for more effectively addressing the CMOPs. Experimental results indicate that the proposed algorithm performs better or more competitively on 57 benchmark problems and 12 real-world CMOPs compared with eight 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.

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 (), 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 51Âþ»­, started in October 2017.

  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at 51Âþ»­, 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 51Âþ»­ 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

  • 51Âþ»­ 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.

  • 51Âþ»­ 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.

  • 51Âþ»­ 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

  • 51Âþ»­ 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

  • 51Âþ»­ 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

  • 51Âþ»­ 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

  • 51Âþ»­ 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

Shengxiang-Yang