Call for papers: Computational Intelligence for Industry 4.0 Special Session at

WCCI 2016 Official Website

This Special Session is dedicated to the latest developments of computational intelligence (CI) for Industry 4.0, the first a-priori engineered (and the fourth) 'Industrial Revolution'.  Focusing on smart manufacturing and cyber-physical systems so far, efforts in Industry 4.0 have lacked smart design and business elements for manufacture that are necessary in completing this unprecedented upgrade of value chain.  Computational intelligence has however provided an extra-numeric, as well as efficiently-numeric, tool to realise this goal.  The Special Session therefore encourages and reports CI applications to Industry 4.0 in the era of interactive cloud computing and data science.


Click here for full details of the Special Session.  Main topics include but are not limited to:


Submission by January 2016 at IEEE CI Congress website


Click here to open a Java window and run the GA_Demo...  

Evolutionary Computer-Automated Design (CAutoD) and Virtual Prototyping for Industry 4.0

Click left on the CAutoD animation - This opens a Java window to run the GA / EA Demo, an interactive courseware to show you step by step how a genetic algorithm works.  You can also watch global convergence in a batch mode, change the population size, crossover rates, mutation rates, selection mechanisms, and/or add a constraint. 

Since 1991, Yun Li has been teaching at University of Glasgow and researching into transforming the passive Computer-Aided Design (CAD) to the pro-active Computer-Automated Design (CAutoD) and machine invention He is Professor of Systems Engineering, specialised in Intelligent Systems (which offer data-driven prospects of computation akin to the human being).  Currently, he and his colleagues are working on cloud computing and big data oriented intelligent CAutoD for "Industry 4.0", with predictive data analytics to extract emerging trends in societal needs and wants and thus to enhance conceptual designs for smart manufacture. Cloud models with a CAutoD server can offer automation functions as services for seamless cyber-physical integration globally.  Application areas include electronic, electrical, mechanical, control, and biomedical engineering, operations management, financial and economic system modelling and optimisation.

Recently published articles for downloading from UofG e-library:

  1. Cloud computing resource scheduling and a survey of its evolutionary methods. ACM Computing Surveys, doi>10.1145/2788397, 2015, 47(4), No.63

  2. Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Applied Soft Computing, 2015, 34(9), pp.286-300.

  3. A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem. IEEE Transactions on Evolutionary Computation, 2013, 17(4), pp.512-527.

  4. Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 2013, 17(2), pp.241-258.

  5. Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, 2015, 45(9). pp.1798-1810

  6. An evolutionary algorithm with double-level archives for multiobjective optimization. IEEE Transactions on Cybernetics, 2014, 45(9, pp.1851-1863

  7. Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Transactions on Industrial Electronics, 2014, 61(12), pp. 7141-7151

  8. Cash flow forecast for South African firms. Review of Development Finance, 2015, 5(1), pp.24-33

  9. Hull form design optimisation for improved efficiency and hydrodynamic performance of 'ship-shaped' offshore vessels, 2015

  10. Key challenges and opportunities in hull form design optimisation for marine and offshore applications, 2015

  11. Dynamic Performance of IEEE 802.15.4 Devices Under Persistent WiFi Traffic, ISBN 9781479983247 (doi:10.1109/RIOT.2015.7104914), 2015, pp.1-6.

  12. From the social learning theory to a social learning algorithm for global optimization, 2014 (doi:10.1109/SMC.2014.6973911).

  13. Heuristically enhanced dynamic neural networks for structurally improving photovoltaic power forecasting, ISBN 9781479966271 (doi:10.1109/IJCNN.2014.6889827), 2014.

  14. Special Issues of 23 latest articles in one pdf: Computational Intelligence Approaches to Robotics, Automation, and Control, Yi Chen, Yun Li, et al, 2015, 302pp, in Mathematical Problems in Engineering,

Some of the CAutoD principles are discussed in papers (Downloadable below or from UofG e-library here):

  1. Design of sophisticated fuzzy logic controllers using genetic algorithms, First IEEE World Congress Computational Intelligence, 1994

  2. Genetic algorithm automated approach to the design of sliding mode control systems, Int J Control, 1996, Li, Y. et al (University of Glasgow)

  3. GA automated design and synthesis of analog circuits with practical constraints, IEEE CEC Evolutionary Computation, 2001, Goh, C. (UofG Singapore) and Li, Y.

  4. CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design, Int J Automation and Computing, 2004

Related evolutionary computing and control publications
(Downloadable below or from UofG e-library here):

  1. Structural system identification using Genetic Programming and a block diagram tool, Electronics Letters, 1996

  2. Nonlinear model structure identification using Genetic Programming, IFAC J Control Engineering Practice, 1998

    YL image

  3. Evolutionary linearisation in the frequency domain, Electronics Letters, 1996, Tan, K.C. (National University of Singapore), et al

  4. Evolutionary system identification in the time domain, IMechE J of Systems & Control Engineering, 1997

  5. Evolutionary Computation Meets Machine Learning: A Survey, IEEE Computational Intelligence Magazine, 2011

  6. Artificial Evolution of neural networks and its application to feedback control, Artificial Intelligence in Engineering, 1996

  7. Grey-box model identification via Evolutionary computing, IFAC J Control Engineering Practice, 2002

  8. Differential Evolution with an Evolution Path: A DEEP Evolutionary Algorithm, IEEE Trans Cybernetics, 2015


  9. Particle Swarm Optimization with an aging leader and challengers, IEEE Trans Evolutionary Computation, 2013

  10. Bi-velocity discrete Particle Swarm Optimization and application..., IEEE Trans Industrial Electronics, 2014

  11. Orthogonal Learning Particle Swarm Optimization, IEEE Trans Evolutionary Computation, 2010

  12. Adaptive Particle Swarm Optimization, IEEE Trans Cybernetics, 2009, Zhang, J. (Sun Yat-sen University), et al

    YL image

  13. Ant Colony Optimization for Wireless Sensor Networks..., IEEE Trans Systems, Man, and Cybernetics, 2011

  14. An efficient Ant Colony system based on receding horizon..., IEEE Trans Intelligent Transportation Systems, 2010

  15. Orthogonal methods based Ant Colony search for solving continuous optimization..., J Computer Science & Technology, 2008

  16. SamACO: variable sampling Ant Colony algorithm for continuous optimization..., IEEE Trans Systems, Man, and Cybernetics, 2010

  17. Protein folding in hydrophobic-polar lattice model: flexible Ant Colony optimization..., Protein and Peptide Letters, 2008


  18. PIDeasy: Patents, software and hardware for PID control: the current art, IEEE Control Systems Magazine, 2006

  19. PID control system analysis, design, and technology, the most popular paper in IEEE Trans Control Systems Tech every month since 2005

    YL image

  20. Book 1: Parallel Processing in a Control Systems Environment, E Rogers & Y Li, Prentice Hall Series on Systems and Control Engineering, 1993, 364pp, ISBN 0-13-651530-4.

  21. Book 2: Real-World Applications of Evolutionary Computing, S Cagnoni, R Poli, & Y Li, et al, Springer-Verlag Lecture Notes in Computer Science, 2000, 396pp, ISBN 978-3-540-67353-8.

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     Yun Li's citations

More on the CAutoD principles

PhD opportunities

PhD is about making an original contribution to knowledge in your field of research, with evidence of systematic study and independent, critical and original thinking.  University of Glasgow has a tradition of original thinking and pushing the boundary of knowledge - our illustrious forebears include James Watt (瓦特), whose 'engineering' (the steam engine with a flyball governor - the world's first industrial feedback controller) at the University in 1769 kicked off the Industrial Revolution, Lord Kelvin (绝对温标 开尔文), Macquorn Rankine (热力学之父 朗肯), John Logie Baird (电视发明者 贝尔德), and Adam Smith (经济学之父 亚当.斯密).  We aim to create technology and knowledge, not just to master it.

(Click here and then "Supervision" for example projects. Past PhD students include Kay Chen Tan (Fellow of IEEE) of National University of Singapore, Cindy Goh of University of Glasgow Singapore, and many others, who enjoy a very successful career in academia, industry and commerce around the world.)

Industry 4.0 / Industrie 4.0 / 工业4.0

Apply online directly: click here

How to apply:

PhD or post-doc visits shorter than 6 months are also very welcome and will be waived of fees: Contact me @ Yun.Li (at)

"Computer-Automated Design", "Design of Simulations", "A-Posteriori Learning", "Systems Economics", "Systems Finance", "Systems Marketing" and "Model-free Predictive Control" are terminology that I have proposed and used to summarise my past, present and future work. You are welcome to use it freely, and acknowledgement is more than welcome.  - Yun Li (李耘