Previous |  Up |  Next

Article

Title: Differential evolution algorithm combined with chaotic pattern search (English)
Author: He, Yaoyao
Author: Zhou, Jianzhong
Author: Lu, Ning
Author: Qin, Hui
Author: Lu, Youlin
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 46
Issue: 4
Year: 2010
Pages: 684-696
Summary lang: English
.
Category: math
.
Summary: Differential evolution algorithm combined with chaotic pattern search(DE-CPS) for global optimization is introduced to improve the performance of simple DE algorithm. Pattern search algorithm using chaotic variables instead of random variables is used to accelerate the convergence of solving the objective value. Experiments on 6 benchmark problems, including morbid Rosenbrock function, show that the novel hybrid algorithm is effective for nonlinear optimization problems in high dimensional space. The comparisons with the standard particle swarm optimization (PSO), differential evolution (DE) and other hybrid algorithms verify DE-CPS algorithm has great superiority. (English)
Keyword: hybrid algorithm
Keyword: differential evolution(DE)
Keyword: chaotic pattern search
Keyword: global optimization
MSC: 49M37
MSC: 65K10
MSC: 90C30
idZBL: Zbl 1203.65090
idMR: MR2722095
.
Date available: 2010-10-22T05:27:02Z
Last updated: 2013-09-21
Stable URL: http://hdl.handle.net/10338.dmlcz/140778
.
Reference: [1] Audet, C., Dennis, J. E.: Pattern search algorithms for mixed variable programming.SIAM J. Optim. 11 (2001), 3, 573–594. MR 1814033, 10.1137/S1052623499352024
Reference: [2] Cai, J. J., Ma, X. Q., Li, X.: Chaotic ant swarm optimization to economic dispatch.Electron. Power Systems Research 77 (2007), 10, 1373–1380. 10.1016/j.epsr.2006.10.006
Reference: [3] Fan, H. Y., Lampinen, J.: A trigonometric mutation operation to differential evolution.J. Global Optim. 27 (2003), 1, 105–129. Zbl 1142.90509, MR 1994565, 10.1023/A:1024653025686
Reference: [4] HART, W. E.: Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization.IEEE Trans. Evol. Comput. 5 (2001), 4, 388–397. 10.1109/4235.942532
Reference: [5] He, Y. Y., Zhou, J. Z., Li, C. S.: A precise chaotic particle swarm optimization algorithm based on improved tent map.ICNC 7 (2008), 569–573.
Reference: [6] He, Y. Y., Zhou, J. Z., Xiang, X. Q.: Comparison of different chaotic maps in particle swarm optimization algorithm for long term cascaded hydroelectric system scheduling.Chaos Solitons Fractals 42 (2009), 5, 3169–3176. Zbl 1198.90184, 10.1016/j.chaos.2009.04.019
Reference: [7] He, Y. Y., Zhou, J. Z., Qin, H.: Flood disaster classification based on fuzzy clustering iterative model and modified differential evolution algorithm.FSKD 3 (2009), 85–89.
Reference: [8] Ji, M. J., Tang, H. W.: Application of chaos in simulated annealing.optimization. Chaos Solitons Fractals 21 (2004), 933–941. Zbl 1045.37054, 10.1016/j.chaos.2003.12.032
Reference: [9] Kaelo, P., Ali, M. M.: A numerical study of some modified differential evolution algorithms.European J. Oper. Res. 169 (2006), 1176–1184. Zbl 1079.90106, MR 2174012, 10.1016/j.ejor.2004.08.047
Reference: [10] Kennedy, J., Eberhan, R. J.: Particle swarm optimization.In: IEEE Internat. Conf on Neural Networks 1995, Vol. 4, pp. 1942–1948.
Reference: [11] Storn, R., Price, K.: Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces.Technical Report TR-95-012, International Computer Science Institute, Berkeley 1995.
Reference: [12] Storn, R., Price, K.: Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces.J. Global Optim. 11 (1997), 341–359. Zbl 0888.90135, MR 1479553, 10.1023/A:1008202821328
Reference: [13] Storn, R., Price, K.: Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces.University of California, Berkeley 2006.
Reference: [14] Tavazoei, M. S., Haeri, M.: Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms.Appl. Math. Comput. 187 (2007), 1076–1085. Zbl 1114.65335, MR 2323114, 10.1016/j.amc.2006.09.087
Reference: [15] Xiang, T., Liao, X. F., Wong, K. W.: An improved particle swarm optimization algorithm combined with piecewise linear chaotic map.Appl. Math. Comput. 190 (2007), 1637–1645. Zbl 1122.65363, MR 2339755, 10.1016/j.amc.2007.02.103
Reference: [16] Yang, D. X., Li, G., Cheng, G. D.: On the efficiency of chaos optimization algorithms for global optimization.Chaos Solitons Fractals 34 (2007), 1366–1375. 10.1016/j.chaos.2006.04.057
Reference: [17] Yuan, X. H., Yuan, Y. B., Zhang, Y. C.: A hybrid chaotic genetic algorithm for short-term hydro system scheduling.Math. Comput. Simul. 59 (2002), 4, 319–327. Zbl 1030.90040, MR 1907567, 10.1016/S0378-4754(01)00363-9
Reference: [18] Yuan, X. F., Wang, Y. N., Wu, L. H.: Pattern search algorithm using chaos and its application.J. of Hunan University (Natural Sciences) 34 (2007), 9, 30-33. Zbl 1150.68455
Reference: [19] Zhang, L., Zhang, C. J.: Hopf bifurcation analysis of some hyperchaotic systems with time-delay controllers.Kybernetika 44 (2008), 1, 35–42. Zbl 1145.93361, MR 2405053
Reference: [20] Zhu, Z. L., Li, S. P., Yu, H.: A new approach to generalized chaos synchronization based on the stability of the error System.Kybernetika 44 (2008), 4, 492–500. Zbl 1172.93015, MR 2459067
.

Files

Files Size Format View
Kybernetika_46-2010-4_7.pdf 1.427Mb application/pdf View/Open
Back to standard record
Partner of
EuDML logo