Abstract
Differential Evolution (DE) is an efficient optimizer in current use. Although many new DE mutant vectors have been proposed by alter the differential operator, there are few works studying the differential operator’s effect in DE algorithm. This paper proposes a correlation between the DE performance and the mutant vector. That is, for a particular mutant vector, increase the number of differential operator would influence the performance of the algorithm linearly. These mutant vectors are evaluated by 23 benchmarks selected from Congress on Evolutionary Computation (CEC) competition. Additionally, this paper proposes an unrestrained method to generate mutant vector. Unlike the old method selects mutually exclusive individuals, the new method allows same individuals appear repeatedly to generate mutant vector. This new method could enhance the potential diversity of the population and improve the performance of DE in general. abstract environment.
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
Neri, F., Tirronen: Recent Advances in Differential Evolution: A Survey and Experimental Analysis. Artif. Intell. Rev. 33(1), 61–106 (2010)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Trans. on Evolutionary Computation (2011), doi:10.1109/TEVC.2010.2059031
Caponio, A., Neri, F.: Differential Evolution with Noise Analyzer. 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. 715–724. Springer, Heidelberg (2009)
Liu, B., Zhang, X., Ma, H.: Hybrid differential evolution for noisy optimization. In: Proc. IEEE Congr. Evol. Comput., vol. 2, pp. 1691–1698 (2009)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. 15(1), 55–66 (2011)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Mezura-Montes, E., Vel azquez-Reyes, J., Coello Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: Proc. Genet. Evol. Comput., pp. 485–492 (2006)
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evolut. Comput. 10(6), 646–657 (2006)
Epitropakis, G., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans. Evol. Comput., 99–119 (2011)
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
Liu, H., Huang, H., Liu, S. (2012). Explore Influence of Differential Operator in DE Mutation with Unrestrained Method to Generate Mutant Vector. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-29353-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29352-8
Online ISBN: 978-3-642-29353-5
eBook Packages: Computer ScienceComputer Science (R0)