Dynamic Time-Linkage Evolutionary Optimization: Definitions and Potential Solutions

Dynamic time-linkage optimization problems (DTPs) are special dynamic optimization 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 (e.g. online scheduling, online vehicle routing, and online optimal control problems), they have received very little attention from the evolutionary dynamic optimization (EDO) research community. Due to this lack of research there are still many characteristics that we do not fully know about DTPs. For example, how should we define and classify DTPs in detail; are 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 chapter these issues will be partially addressed. First, we will propose a detailed definition framework to help characterising DOPs and DTPs. Second, we will identify a new and challenging class of DTPs where it might not be possible to solve the problems using traditional methods. Third, 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]  Johann Dréo,et al.  An ant colony algorithm aimed at dynamic continuous optimization , 2006, Appl. Math. Comput..

[2]  Nanlin Jin,et al.  Adaptive farming strategies for dynamic economic environment , 2007, 2007 IEEE Congress on Evolutionary Computation.

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

[4]  Gary G. Yen,et al.  Dynamic Evolutionary Algorithm With Variable Relocation , 2009, IEEE Transactions on Evolutionary Computation.

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

[6]  Xiaohong Jiang,et al.  Ant-based survivable routing in dynamic WDM networks with shared backup paths , 2006, The Journal of Supercomputing.

[7]  Edmund K. Burke,et al.  On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport , 2008, J. Sched..

[8]  Hyungsuck Cho,et al.  Microassembly of micro peg and hole using an optimal visual proportional differential controller , 2008 .

[9]  Johann Dréo,et al.  Fitting of an Ant Colony approach to Dynamic Optimization through a new set of test functions , 2007 .

[10]  Susana Cecilia Esquivel,et al.  An Evolutionary Algorithm to Track Changes of Optimum Value Locations in Dynamic Environments , 2004 .

[11]  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.

[12]  Shengxiang Yang,et al.  Evolutionary Computation in Dynamic and Uncertain Environments , 2007, Studies in Computational Intelligence.

[13]  T. Back,et al.  On the behavior of evolutionary algorithms in dynamic environments , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

[15]  Hitoshi Kanoh,et al.  Dynamic route planning for car navigation systems using virus genetic algorithms , 2007, Int. J. Knowl. Based Intell. Eng. Syst..

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

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

[18]  Amitabha Das NETWORKING 2008, Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet , 7th International IFIP-TC6 Networking Conference, Singapore, May 5-9, 2008, Proceedings , 2008, Networking.

[19]  Ning Wang,et al.  Adaptive Multi-topology IGP Based Traffic Engineering with Near-Optimal Network Performance , 2008, Networking.

[20]  Von der Fakult Evolutionary Algorithms and Dynamic Optimization Problems , 2003 .

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

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

[23]  Miguel Rocha,et al.  Evolutionary algorithms for static and dynamic optimization of fed-batch fermentation processes , 2005 .

[24]  Mohammad Safdar Baloch,et al.  Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms , 2007 .

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

[26]  Xin Yao,et al.  Attributes of Dynamic Combinatorial Optimisation , 2008, SEAL.

[27]  Xin Yao,et al.  On the role of modularity in evolutionary dynamic optimisation , 2010, IEEE Congress on Evolutionary Computation.

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

[29]  Kang-Zhi Liu,et al.  A new model predictive control approach to DC-DC converters based on combinatory optimization , 2008, 2008 SICE Annual Conference.

[30]  Tim Hendtlass,et al.  Solving Dynamic Single-Runway Aircraft Landing Problems With Extremal Optimisation , 2007, 2007 IEEE Symposium on Computational Intelligence in Scheduling.

[31]  B. De Schutter,et al.  Least-cost model predictive control of residential energy resources when applying μmCHP , 2007, 2007 IEEE Lausanne Power Tech.

[32]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[33]  Karsten Weicker,et al.  An Analysis of Dynamic Severity and Population Size , 2000, PPSN.

[34]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[35]  Peter A. N. Bosman,et al.  Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case , 2007, GECCO '07.

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

[37]  David Z. Zhang,et al.  Agent-based model for optimising supply-chain configurations , 2008 .