Abstract
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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahmad, A.Z., Liu, K.-Z.: A new model predictive control approach to dc-dc converters based on combinatory optimization. In: Proceedings - 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008, Orlando, FL, United states, pp. 460–465 (2008)
Akanle, O.M., Zhang, D.Z.: Agent-based model for optimising supply-chain configurations. International Journal of Production Economics 115(2), 444–460 (2008)
Aragon, V.S., Esquivel, S.C.: An evolutionary algorithm to track changes of optimum value locations in dynamic environments. Journal of Computer Science and Technology 4(3), 127–134 (2004)
Jason, A.D., Atkin, E.K., Burke, J.S.: Greenwood, and Dale Reeson. On-line decision support for take-off runway scheduling with uncertain taxi times at london heathrow airport. Journal of Scheduling 11(5), 323–346 (2008)
Bäck, T.: On the behavior of evolutionary algorithms in dynamic environments. In: IEEE International Conference on Evolutionary Computation, pp. 446–451. IEEE (1998)
Bosman, P.A.N.: Learning, anticipation and time-deception in evolutionary online dynamic optimization. In: Yang, S., Branke, J. (eds.) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization (2005)
Bosman, P.A.N.: Learning and anticipation in online dynamic optimization. In: Yang, S., Ong, Y.-S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 129–152. Springer (2007)
Bosman, P.A.N., Poutré, H.L.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1165–1172. ACM, New York (2007)
Branke, J., Mattfeld, D.: Anticipation and flexibility in dynamic scheduling. International Journal of Production Research 43(15), 3103–3129 (2005)
Dreo, J., Siarry, P.: An ant colony algorithm aimed at dynamic continuous optimization. Applied Mathematics and Computation 181(1), 457–467 (2006)
Fiacchini, M., Alamo, T., Alvarado, I., Camacho, E.F.: Safety verification and adaptive model predictive control of the hybrid dynamics of a fuel cell system. International Journal of Adaptive Control and Signal Processing 22(3), 142–160 (2008)
Houwing, M., Negenborn, R.R., Heijnen, P.W., De Schutter, B., Hellendoorn, H.: Least-cost model predictive control of residential energy resources when applying μCHP. In: Proceedings of the Power Tech 2007 Conference, Lausanne, Switzerland, Paper 291 (July 2007)
Jin, N., Termansen, M., Hubacek, K., Holden, J., Kirkby, M.: Adaptive farming strategies for dynamic economic environment. In: Proceedings of the IEEE Congress on Evolutionary Computation CEC 2007, pp. 1213–1220 (2007)
Kanoh, H.: Dynamic route planning for car navigation systems using virus genetic algorithms. International Journal of Knowledge-based and Intelligent Engineering Systems 11(1), 65–78 (2007)
Long, C.E., Polisetty, P.K., Gatzke, E.P.: Deterministic global optimization for nonlinear model predictive control of hybrid dynamic systems. International Journal of Robust and Nonlinear Control 17(13), 1232–1250 (2007)
Morimoto, T., Ouchi, Y., Shimizu, M., Baloch, M.S.: Dynamic optimization of watering satsuma mandarin using neural networks and genetic algorithms. Agricultural Water Management 93(1-2), 1–10 (2007)
Moser, I., Hendtlass, T.: Solving dynamic single-runway aircraft landing problems with extremal optimisation. In: IEEE Symposium on Computational Intelligence in Scheduling (2007)
Ngo, S.H., Jiang, X., Le, V.T., Horiguchi, S.: Ant-based survivable routing in dynamic wdm networks with shared backup paths. The Journal of Supercomputing 36(3), 297–307 (2006)
Nguyen, T.T.: Continuous Dynamic Optimisation Using Evolutionary Algorithms. PhD thesis, School of Computer Science, University of Birmingham (2011), http://etheses.bham.ac.uk/1296 , http://www.cs.bham.ac.uk/txn/theses/phd_thesis_nguyen.pdf
Nguyen, T.T., Yao, X.: Dynamic Time-Linkage Problems Revisited. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 735–744. Springer, Heidelberg (2009)
Rocha, M., Neves, J., Veloso, A.: Evolutionary algorithms for static and dynamic optimization of fed-batch fermentation processes. In: Ribeiro, B., et al. (eds.) Adaptive and Natural Computing Algorithms, pp. 288–291. Springer (2005)
Rohlfshagen, P., Yao, X.: Attributes of Dynamic Combinatorial Optimisation. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 442–451. Springer, Heidelberg (2008)
Rohlfshagen, P., Yao, X.: On the role of modularity in evolutionary dynamic optimisation. In: Proceedings of the 2010 IEEE Wolrd Congress on Computational Intelligence, WCCI 2010, Spain, pp. 3539–3546 (2010)
Sonntag, C., Su, W., Stursberg, O., Engell, S.: Optimized start-up control of an industrial-scale evaporation system with hybrid dynamics. Control Engineering Practice 16(8), 976–990 (2008)
Summers, S., Bewley, T.R.: Mpdopt: A versatile toolbox for adjoint-based model predictive control of smooth and switched nonlinear dynamic systems. In: Proceedings of the 46th IEEE Conference on Decision and Control 2007, pp. 4785–4790 (2007)
Tafazoli, S., Sun, X.: Hybrid system state tracking and fault detection using particle filters. IEEE Transactions on Control Systems Technology 14(6), 1078–1087 (2006)
Tfaili, W., Dréo, J., Siarry, P.: Fitting of an ant colony approach to dynamic optimization through a new set of test functions. International Journal of Computational Intelligence Research 3, 205–218 (2007)
Ursem, R.K., Krink, T., Jensen, M.T., Michalewicz, Z.: Analysis and modeling of control tasks in dynamic systems. IEEE Transactions on Evolutionary Computation 6(4), 378–389 (2002)
Wang, J., Tao, X., Cho, H.: Microassembly of micro peg and hole using an optimal visual proportional differential controller. Proceedings of the Institution of Mechanical Engineers, Part B (Journal of Engineering Manufacture) 222(B9), 1171–1180 (2008)
Wang, N., Ho, K.-H., Pavlou, G.: Adaptive Multi-topology IGP Based Traffic Engineering with Near-Optimal Network Performance. In: Das, A., Pung, H.K., Lee, F.B.S., Wong, L.W.C. (eds.) NETWORKING 2008. LNCS, vol. 4982, pp. 654–666. Springer, Heidelberg (2008)
Weicker, K.: An Analysis of Dynamic Severity and Population Size. In: Deb, K., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 159–168. Springer, Heidelberg (2000)
Weicker, K.: Evolutionary algorithms and dynamic optimization problems. Der Andere Verlag (2003)
Woldesenbet, Y.G., Yen, G.G.: Dynamic evolutionary algorithm with variable relocation. IEEE Transactions on Evolutionary Computation 13(3), 500–513 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nguyen, T.T., Yao, X. (2013). Dynamic Time-Linkage Evolutionary Optimization: Definitions and Potential Solutions. In: Alba, E., Nakib, A., Siarry, P. (eds) Metaheuristics for Dynamic Optimization. Studies in Computational Intelligence, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30665-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-30665-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30664-8
Online ISBN: 978-3-642-30665-5
eBook Packages: EngineeringEngineering (R0)