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:
Computer intelligence or machine learning for cyber-physical systems;
Computer-automated design, machine learning or intelligent search for Industry 4.0;
Computational intelligence for smart design for smart manufacture;
Computational intelligence for Industry 4.0 in cloud and big data environments;
Computational intelligence and data science applications to marketing for design;
Computational intelligence and data science for marketing and service in Industry 4.0 value chain;
Computational intelligence or other learning techniques for Industry 4.0 business informatics and risk management;
Computational intelligence for Industry 4.0 digital economy;
Evolutionary distributed or cloud computing for interactive product design and marketing;
Evolutionary big data interaction for predictive product design and marketing.
Submission by 15 January 2016 at IEEE CI Congress website http://www.wcci2016.org/
Key Challenges and Future Directions of Evolutionary Computation - Workshop at WCCI'16, Vancouver, Canada, paper submission by 15 Jan 2016
Energy-Efficient Design for Smart Manufacturing and Industry 4.0 - Special Issue of Energies (Impact Factor 2.1), rolling publication upon acceptance
Computer Intelligence and Neuroscience for Industry 4.0 Applications - Special Issue of Journal of CI and Neuroscience, submission by 22 Jan 2016
This is an interactive courseware to show users 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.Tweet
Evolutionary Computer-Automated Design (CAutoD) and Virtual Prototyping for Industry 4.0
Since 1991, the author (Yun Li) has been teaching at University of Glasgow and wrote this applet for his "Neural and Evolutionary Computing" course. His Intelligent Systems group research into transforming the passive Computer-Aided Design (CAD) to the pro-active Computer-Automated Design (CAutoD) and machine invention, especially for "Industry 4.0". With data-driven prospects of computation akin to the human being, CAutoD can offer automation functions as services for seamless cyber-physical integration globally, and is applicable to electronic, electrical, mechanical, control, and biomedical engineering, operations management, financial and economic system modelling and optimisation.
IEEE Xplore Proc. 21st IEEE International Conference Automation & Computing - Special Session and Forum on Industry 4.0, Y Li & H Yu, Sept 2015, Glasgow
IEEE Xplore proceedings of 90 papers on Genetic Algorithms in Engineering Systems: Innovations and Applications, Peter Fleming, Yun Li, et al, 1997, Glasgow
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.
Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys, 2015, 47(4), No.63
Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Applied Soft Computing, 2015, 34(9), pp.286-300.
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.
Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 2013, 17(2), pp.241-258.
Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, 2015, 45(9). pp.1798-1810
An evolutionary algorithm with double-level archives for multiobjective optimization. IEEE Transactions on Cybernetics, 2014, 45(9, pp.1851-1863
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
Cash flow forecast for South African firms. Review of Development Finance, 2015, 5(1), pp.24-33
Design of sophisticated fuzzy logic controllers using genetic algorithms, First IEEE World Congress Computational Intelligence, 1994
Genetic algorithm automated approach to the design of sliding mode control systems, Int J Control, 1996, Li, Y. et al (University of Glasgow)
GA automated design and synthesis of analog circuits with practical constraints, IEEE CEC Evolutionary Computation, 2001, Goh, C. (UofG Singapore) and Li, Y.
CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design, Int J Automation and Computing, 2004
Structural system identification using Genetic Programming and a block diagram tool, Electronics Letters, 1996
identification using Genetic Programming,
IFAC J Control
Engineering Practice, 1998
Evolutionary linearisation in the frequency domain, Electronics Letters, 1996, Tan, K.C. (National University of Singapore), et al
Evolutionary system identification in the time domain, IMechE J of Systems & Control Engineering, 1997
Evolutionary Computation Meets Machine Learning: A Survey, IEEE Computational Intelligence Magazine, 2011
Artificial Evolution of neural networks and its application to feedback control, Artificial Intelligence in Engineering, 1996
Grey-box model identification via Evolutionary computing, IFAC J Control Engineering Practice, 2002
Differential Evolution with an Evolution
Path: A DEEP Evolutionary Algorithm,
Particle Swarm Optimization with an aging leader and challengers, IEEE Trans Evolutionary Computation, 2013
Bi-velocity discrete Particle Swarm Optimization and application..., IEEE Trans Industrial Electronics, 2014
Orthogonal Learning Particle Swarm Optimization, IEEE Trans Evolutionary Computation, 2010
Trans Cybernetics, 2009,
Zhang, J. (Sun
Yat-sen University), et al
Ant Colony Optimization for Wireless Sensor Networks..., IEEE Trans Systems, Man, and Cybernetics, 2011
An efficient Ant Colony system based on receding horizon..., IEEE Trans Intelligent Transportation Systems, 2010
Orthogonal methods based Ant Colony search for solving continuous optimization..., J Computer Science & Technology, 2008
SamACO: variable sampling Ant Colony algorithm for continuous optimization..., IEEE Trans Systems, Man, and Cybernetics, 2010
in hydrophobic-polar lattice model: flexible Ant Colony optimization...,
and Peptide Letters, 2008
PIDeasy: Patents, software and hardware for PID control: the current art, IEEE Control Systems Magazine, 2006
system analysis, design, and technology,
the every month since 2005
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.
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.
See also Evolutionary algorithms in engineering applications, D Dasgupta, Z Michalewicz, eds., Springer.
Web of Science (SCI)
Yun Li is Professor of Systems Engineering at University of Glasgow. He received his PhD in parallel computing and control from University of Strathclyde in 1990. Following a period as Consultant Engineer with U.K. National Engineering Laboratory in 1989 and as post-doctoral Research Engineer with Industrial Systems and Control Ltd in 1990, he joined University of Glasgow as Lecturer in 1991. Since 1992, he has trained over 30 PhDs, one of whom has been honoured IEEE Fellow. Dr Li developed a "Neural and Evolutionary Computing" course in 1995 and a popular online interactive courseware GA Demo in 1997. In 1998, he established and chaired the IEEE CACSD Evolutionary Computation Working Group. He also established the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on systems, control and drives, and served on the EvoNet Management Board. In 2011, Professor Li went to Singapore as Founding Director to establish and lead University of Glasgow Singapore, the first overseas subsidiary in the University's 560-year history. Later, he acted as interim/founding director of the University of Glasgow-University of Electronic Science and Technology of China joint education programme in Chengdu. He has over 200 publications, one of which is elected by Thomson Reuters into "Research Front in Computer Science", one into "Research Front in Engineering", four into "Essential Science Indicators" (ESI), one has been the most popular paper in IEEE Transactions on Control Systems Technology every month since publication in 2005, and one among the five most popular in IEEE Trans Systems, Man & Cybernetics - B. Professor Li is also the University's third "Top Author", an Associate Editor of IEEE Trans Evolutionary Computation and other journals, a Chartered Engineer in the U.K., and an overseas referee for China's "Yangtze River Scholars Program" (教育部长江学者海外评委) and Singapore's National Research Foundation and Ministry of Education. (李耘中文网页, 世界百强格拉斯哥大学简介)