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Parameter analysis based on stochastic model for differential evolution algorithm. (English) Zbl 1204.65072
Summary: A stochastic model is used to describe and analyze the evolution process of differential evolution (DE) for numerical optimization. With the model, it illustrates how the probability distribution of the whole population is changed by mutation, selection and crossover operations. Based on the theoretical analysis, some guidelines about the parameter setting for DE are provided. In addition, numerical simulations are carried out to verify the conclusions drawn from model analysis.
65K05Mathematical programming (numerical methods)
90C15Stochastic programming
[1]Storn, R.; Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization 11, No. 4, 341-359 (1997) · Zbl 0888.90135 · doi:10.1023/A:1008202821328
[2]Chiou, J. P.; Chang, C. F.; Su, C. T.: Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems, IEEE transactions on power systems 20, No. 2, 668-674 (2005)
[3]Nobakhti, A.; Wang, H.: A simple self-adaptive differential evolution algorithm with application on the ALSTOM, Applied soft computing 8, No. 1, 350-370 (2008)
[4]Eiben, A. E.; Hinterding, R.; Michalewicz, Z.: Parameter control in evolutionary algorithms, IEEE transactions on evolutionary computation 3, No. 2, 124-141 (1999)
[5]Ali, M. M.: Differential evolution with preferential crossover, European journal of operational research 181, No. 3, 1137-1147 (2007) · Zbl 1123.90058 · doi:10.1016/j.ejor.2005.06.077
[6]K. Price, R. Storn, DE homepage, http://www.icsi.berkeley.edu/sim;storn/code.html, 2001.