Abstract
Differential evolution (DE) is a simple, yet efficient global optimization algorithm. As the standard DE and most of its variants operate in the continuous space, this paper presents a modified binary differential evolution algorithm (MBDE) to tackle the binary-coded optimization problems. A novel probability estimation operator inspired by the concept of distribution of estimation algorithm is developed, which enables MBDE to manipulate binary-valued solutions directly and provides better tradeoff between exploration and exploitation cooperated with the other operators of DE. The effectiveness and efficiency of MBDE is verified in application to numerical optimization problems. The experimental results demonstrate that MBDE outperforms the discrete binary DE, the discrete binary particle swarm optimization and the binary ant system in terms of both accuracy and convergence speed on the suite of benchmark functions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Storn, R., Price, K.V.: Differential Evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces. Technology Report. Berkeley, CA, TR-95-012 (1995)
Vesterstrom, J., Thomsen, R.: A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1980–1987. IEEE Press, Los Alamitos (2004)
Rekanos, I.T.: Shape Reconstruction of a Perfectly Conducting Scatterer Using Differential Evolution and Particle Swarm Optimization. IEEE Transaction on Geoscience and Remote Sensing 46, 1967–1974 (2008)
Ponsich, A., Coello, C.A.: Differential Evolution performances for the solution of mixed integer constrained Process Engineering problems. Applied Soft Computing (2009), doi: 10.1016/j.asoc.2009.11.030
Liu, J., Lampinen, J.: A Fuzzy Adaptive Differential Evolution Algorithm. Soft Comput. 9, 448–462 (2005)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transaction on Evolutionary Computation 13, 398–417 (2009)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential Evolution Using a Neighborhood-Based Mutation Operator. IEEE Transaction on Evolutionary Computation 13, 526–553 (2009)
Pampara, G., Franken, N., Engelbrecht, A.P.: Combining Particle Swarm Optimisation with Angle Modulation to Solve Binary Problems. In: The 2005 IEEE Congress on Evolutionary Computation, pp. 89–96. IEEE Press, Los Alamitos (2005)
Pampará, G., Engelbrecht, A.P., Franken, N.: Binary Differential Evolution. In: Proceedings of IEEE Transaction on Evolutionary Computation, pp. 1873–1879 (2006)
He, S.X., Han, L.: A novel binary differential evolution algorithm based on artificial immune system. In: IEEE Congress on Evolutionary Computation, pp. 2267–2272. IEEE Press, Los Alamitos (2007)
Gong, T., Tuson, A.L.: Differential Evolution for Binary Encoding. Soft Computing in Industrial Applications, ASC 39, 251–262 (2007)
Chen, P., Li, J., Liu, Z.M.: Solving 0-1 Knapsack Problems by a Discrete Binary Version of Differential Evolution. In: Second International Symposium on Intelligent Information Technology Application, IITA 2008, pp. 513–516. IEEE Press, Los Alamitos (2008)
Kennedy, J., Eberhart, R.: A Discrete Binary Version of the Particle Swarm Optimization. In: The 1997 Conference on System, man and Cybernetics, pp. 4104–4108. IEEE Press, Los Alamitos (1997)
Kong, M., Tian, P.: A Binary Ant Colony Optimization for the Unconstrained Function Optimization Problem. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 682–687. Springer, Heidelberg (2005)
Pelikan, M., Goldberg, D.E., Lobo, F.G.: A Survey of Optimization by Building and Using Probabilistic Models. Computational Optimization and Applications 21, 5–20 (2002)
Baluja, S.: Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Technical Report CMU-CS-94-163, Pittsburgh, PA: Carnegie Mellon University (1994)
Wang, L., Wang, X.T., Fu, J.Q., Zhen, L.L.: A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application. Journal of Software 3, 28–35 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Fu, X., Menhas, M.I., Fei, M. (2010). A Modified Binary Differential Evolution Algorithm. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-15597-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15596-3
Online ISBN: 978-3-642-15597-0
eBook Packages: Computer ScienceComputer Science (R0)