Abstract
Differential evolution algorithm with composite trial vector generation strategies and control parameters has been proposed recently. The performance of this algorithm is claimed to be better or competitive in comparison with the state-of-the-art variants of differential evolution. When we attempted to implement the algorithm according to the published description, several modified variants appear to follow the description of the algorithm. These variants of the algorithm were compared experimentally in benchmark problems. One of newly proposed variants outperforms the other variants significantly, including the variant used by the authors of the algorithm in their published experimental comparison.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Differential Evolution
- Benchmark Problem
- Acceptable Solution
- Differential Evolution Algorithm
- Trial Vector
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.
References
Brest, J., Greiner, S., Boškovič, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10, 646–657 (2006)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation 13, 526–553 (2009)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15, 27–54 (2011)
Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 11, 1679–1696 (2011)
Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review 33, 61–106 (2010)
Price, K.V.: An introduction to differential evolution. In: New Ideas in Optimization, pp. 293–298. McGraw-Hill, London (1999)
Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer (2005)
Qin, A., Huang, V., Suganthan, P.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13, 398–417 (2009)
Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11, 341–359 (1997)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization (2005), http://www.ntu.edu.sg/home/epnsugan/
Tvrdík, J.: A comparison of control-parameter-free algorithms for single-objective optimization. In: Matousek, R. (ed.) 16th International Conference on Soft Computing Mendel 2010, pp. 71–77 (2010)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation 15, 55–66 (2011)
Zhang, J., Sanderson, A.C.: JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13, 945–958 (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 paper
Cite this paper
Tvrdík, J. (2013). Modifications of Differential Evolution with Composite Trial Vector Generation Strategies. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-32922-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32921-0
Online ISBN: 978-3-642-32922-7
eBook Packages: EngineeringEngineering (R0)