Dynamic Time-Linkage Problems - The Challenges

Dynamic time-linkage optimisation problems (DTPs) are special dynamic optimisation problems (DOPs) where the current solutions chosen by the solver can influence how the problems might change in the future. Although DTPs are very common in real-world applications, they have received very little attention from the fleld of evolutionary and meta-heuristic optimisation. Due to this lack of research there are still many characteristics that we do not fully know about DTPs. For example, is there any characteristics of DTPs that we do not know; with these characteristics are DTPs still solvable; and what is the appropriate strategy to solve them. In this paper these issues will be partially addressed. First, we will identify a new and challenging class of DTPs where it might not be possible to solve the problems using traditional methods. Second, an approach to solve this class of problems under certain circumstances will be suggested and experiments to verify the hypothesis will be carried out. Two test problems will be proposed to simulate the property of this new class of DTPs, and discussions of real-world applications will be introduced.

[1]  Peter A. N. Bosman,et al.  Learning, anticipation and time-deception in evolutionary online dynamic optimization , 2005, GECCO '05.

[2]  E. Gatzke,et al.  Deterministic global optimization for nonlinear model predictive control of hybrid dynamic systems , 2007 .

[3]  Jürgen Branke *,et al.  Anticipation and flexibility in dynamic scheduling , 2005 .

[4]  Zbigniew Michalewicz,et al.  Analysis and modeling of control tasks in dynamic systems , 2002, IEEE Trans. Evol. Comput..

[5]  Sean Summers,et al.  MPDopt: A versatile toolbox for adjoint-based model predictive control of smooth and switched nonlinear dynamic systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[6]  Eduardo F. Camacho,et al.  Safety verification and adaptive model predictive control of the hybrid dynamics of a fuel cell system , 2008 .

[7]  Sebastian Engell,et al.  Optimized start-up control of an industrial-scale evaporation system with hybrid dynamics ☆ , 2008 .

[8]  Kang-Zhi Liu,et al.  A new model predictive control approach to DC-DC converters based on combinatory optimization , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[9]  Xin Yao,et al.  Dynamic Time-Linkage Problems Revisited , 2009, EvoWorkshops.

[10]  Trung Thanh Nguyen,et al.  Continuous dynamic optimisation using evolutionary algorithms , 2011 .

[11]  Peter A. N. Bosman Learning and Anticipation in Online Dynamic Optimization , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[12]  Xuehong Sun,et al.  Hybrid System State Tracking and Fault Detection Using Particle Filters , 2006, IEEE Transactions on Control Systems Technology.