Abstract
Constraint satisfaction problems (CSPs) underpin many science and engineering applications. Recently introduced intelligent constraint handling evolutionary algorithm (ICHEA) in [14] has demonstrated strong potential in solving them through evolutionary algorithms (EAs). ICHEA outperforms many other evolutionary algorithms to solve CSPs with respect to success rate (SR) and efficiency. This paper is an enhancement of ICHEA to improve its efficiency and SR further by an enhancement of the algorithm to deal with local optima obstacles. The enhancement also includes a capability to handle dynamically introduced constraints without restarting the whole algorithm that uses the knowledge from already solved constraints using an incremental approach. Experiments on benchmark CSPs adapted as dynamic CSPs has shown very promising results.
This is a preview of subscription content, log in via an institution to check access.
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
Brest, J., et al.: Dynamic optimization using Self-Adaptive Differential Evolution. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 415–422 (2009)
Craenen, B.G.W., et al.: Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 7, 424–444 (2003)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Eiben, A.E.: Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions. In: Theoretical Aspects of Evolutionary Computing, pp. 13–58 (2001)
El Rhalibi, A., Kelleher, G.: An approach to dynamic vehicle routing, rescheduling and disruption metrics. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3613–3618 (2003)
Karimi, J., et al.: A new hybrid approach for dynamic continuous optimization problems. Applied Soft Computing 12, 1158–1167 (2012)
Kramer, O.: A Review of Constraint-Handling Techniques for Evolution Strategies. Applied Computational Intelligence and Soft Computing, 1–11 (2010)
Li, C., et al.: Benchmark Generator for CEC’2009 Competition on Dynamic Optimization (2008)
Li, T., et al.: Dynamic Constraint Satisfaction Approach to Hybrid Flowshop Rescheduling. In: 2007 IEEE International Conference on Automation and Logistics, pp. 818–823 (2007)
Liu, H., et al.: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl. Soft Comput., 629–640 (2010)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4, 1–32 (1996)
Nguyen, T., Yao, X.: Continuous Dynamic Constrained Optimisation - The Challenges. IEEE Transactions on Evolutionary Computation 99, 1 (2012)
Dechter, R.: Constraint networks. In: Encyclopedia of Artificial Intelligence, pp. 276–285. John Wiley & Sons, Ltd., New York (1992)
Sharma, A., Sharma, D.: ICHEA – A Constraint Guided Search for Improving Evolutionary Algorithms. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part I. LNCS, vol. 7663, pp. 269–279. Springer, Heidelberg (2012)
The COCONUT Benchmark, http://www.mat.univie.ac.at/~neum/glopt/coconut/Bench-mark/Benchmark.html
Tessema, B., Yen, G.G.: A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, pp. 246–253 (2006)
Verfaillie, G., Jussien, N.: Constraint Solving in Uncertain and Dynamic Environments: A Survey. Constraints, 253–281 (2005)
Wang, H., Wang, D.-W., Yang, S.: Triggered Memory-Based Swarm Optimization in Dynamic Environments. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 637–646. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sharma, A., Sharma, D. (2012). An Incremental Approach to Solving Dynamic Constraint Satisfaction Problems. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-34487-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
eBook Packages: Computer ScienceComputer Science (R0)