Abstract
In this paper we show that the technique of handling boundary constraints has a significant influence on the efficiency of the Differential Evolution method. We study the effects of applying several such techniques taken from the literature. The comparison is based on experiments performed for a standard DE/rand/1/bin strategy using the CEC2005 benchmark. The paper reports the results of experiments and provides their simple statistical analysis. Among several constraint handling methods, a winning approach is to repeat the differential mutation by resampling the population until a feasible mutant is obtained. Coupling the aforementioned method with a simple DE/rand/1/bin strategy allows to achieve results that outperform in many cases results of almost all other methods tested during the CEC2005 competition, including the original DE/rand/1/bin strategy.
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
Price, K., et al.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Neri, F., Tirronen, V.: Recent advances in Differential Evolution: a survey and experimental analysis. Artificial Intelligence Rev. 33(1-2), 61–106 (2010)
Qin, A.K., et al.: Differential Evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evolutionary Computation 13(2), 398–417 (2009)
Liu, J., Lampinen, J.: A Fuzzy Adaptive Differential Evolution algorithm. Soft Computing 9(6), 448–462 (2005)
Zhang, J., Sanderson, A.C.: JADE: adaptive Differential Evolution with optional external archive. IEEE Trans. Evolutionary Computation 13(5), 945–958 (2009)
Doumpos, M., et al.: An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method. Eur. J. of Operational Research 199(2), 496–505 (2009)
Rönkkönen, J., et al.: Real-parameter optimization with differential evolution. In: CEC 2005. IEEE, Los Alamitos (2005)
Brest, J., et al.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evolutionary Computation 10(6), 646–657 (2006)
Karabogal, N., Cetinkayal, B.: Design of digital FIR filters using Differential Evolution algorithm. Circuits, Systems, Signal Processing 25(5), 649–660 (2006)
Hansen, N.: Compilation of results on the 2005 CEC benchmark function set (2005), http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/compareresults.pdf
Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Tech. Univ. (2005)
Bui, L.T., et al.: Comparing two versions of differential evolution in real parameter optimization. In: CEC 2005. IEEE, Los Alamitos (2005)
Qin, A., Suganthan, P.: Self-adaptive differential evolution algorithm for numerical optimization. In: CEC 2005. IEEE, Los Alamitos (2005)
Martines, C.G., Lozano, M.: Hybrid real-coded genetic algorithms with female and male differentiation. In: CEC 2005. IEEE, Los Alamitos (2005)
Molina, D., et al.: Adaptive local search parameters for real-coded memetic algorithms. In: CEC 2005. IEEE, Los Alamitos (2005)
Ballester, P., et al.: Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. In: CEC 2005. IEEE, Los Alamitos (2005)
Sinha, A., et al.: A population-based, steady-state procedure for real-parameter optimization. In: CEC 2005. IEEE, Los Alamitos (2005)
Posik, P.: Real parameter optimization using mutation step co-evolution. In: CEC 2005. IEEE, Los Alamitos (2005)
Liang, J., Suganthan, P.: Dynamic multi-swarm particle swarm optimizer with local search. In: CEC 2005. IEEE, Los Alamitos (2005)
Yuan, B., Gallagher, M.: Experimental results for the special session on real-parameter optimization at CEC 2005: A simple, continuous EDA. In: CEC 2005. IEEE, Los Alamitos (2005)
Auger, A., et al.: A restart CMA evolution strategy with increasing population size. In: CEC 2005. IEEE, Los Alamitos (2005)
Auger, A., et al.: Performance evaluation of an advanced local search evolutionary algorithm. In: CEC 2005. IEEE, Los Alamitos (2005)
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
Arabas, J., Szczepankiewicz, A., Wroniak, T. (2010). Experimental Comparison of Methods to Handle Boundary Constraints in Differential Evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)