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2015 | OriginalPaper | Buchkapitel

Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method

verfasst von : Laizhong Cui, Genghui Li, Li Li, Qiuzhen Lin, Jianyong Chen, Nan Lu

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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Abstract

Differential evolution (DE) algorithm is a very effective and efficient approach for solving global numerical optimization problems. However, DE still suffers from some limitations. Moreover, the performance of DE is sensitive to its mutation strategy and associated parameters. In this paper, an enhanced differential evolution algorithm called EDE is proposed, which including a new mutation strategy and a new control method of parameters. Compared with other DE algorithms including four classical DE and two state-of-the-art DE variants on ten numerical benchmarks, the experiment results indicate that the performance of EDE is better than those of the other algorithms.

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Literatur
1.
Zurück zum Zitat Storn, R., Price, K.V.: Differential evolution: a simple and efficient heuristic for global optimization over continuous space. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATH Storn, R., Price, K.V.: Differential evolution: a simple and efficient heuristic for global optimization over continuous space. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATH
2.
Zurück zum Zitat Gao, Z., Pan, Z., Gao, J.: A new highly efficient differential evolution scheme and its application to waveform inversion. IEEE Geosci. Remote Sens. Lett. 11(10), 1702–1706 (2014)CrossRef Gao, Z., Pan, Z., Gao, J.: A new highly efficient differential evolution scheme and its application to waveform inversion. IEEE Geosci. Remote Sens. Lett. 11(10), 1702–1706 (2014)CrossRef
3.
Zurück zum Zitat Tenaglia, G.C., Lebensztajn, L.: A multiobjective approach of differential evolution optimization applied to electromagnetic problems. IEEE Trans. Magn. 50(2), 625–628 (2014)CrossRef Tenaglia, G.C., Lebensztajn, L.: A multiobjective approach of differential evolution optimization applied to electromagnetic problems. IEEE Trans. Magn. 50(2), 625–628 (2014)CrossRef
4.
Zurück zum Zitat Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)CrossRef Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)CrossRef
5.
Zurück zum Zitat Yu, W.J., Shen, M., Chen, W.N., Zhan, Z.H., Gong, Y.J., Lin, Y.: Differential evolution with two-level parameter adaption. IEEE Trans. Cybern. 44(7), 1080–1099 (2014)CrossRef Yu, W.J., Shen, M., Chen, W.N., Zhan, Z.H., Gong, Y.J., Lin, Y.: Differential evolution with two-level parameter adaption. IEEE Trans. Cybern. 44(7), 1080–1099 (2014)CrossRef
6.
Zurück zum Zitat Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRef Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRef
7.
Zurück zum Zitat Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Comput. 9(6), 448–462 (2005)CrossRefMATH Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Comput. 9(6), 448–462 (2005)CrossRefMATH
8.
Zurück zum Zitat 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. Evol. Comput. 10(6), 646–657 (2006)CrossRef 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. Evol. Comput. 10(6), 646–657 (2006)CrossRef
9.
Zurück zum Zitat Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Trans. Cybern. 43(6), 2066–2081 (2013)CrossRef Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Trans. Cybern. 43(6), 2066–2081 (2013)CrossRef
10.
Zurück zum Zitat Cai, Y., Wang, J.: Differential evolution with neighborhood and direction information for numerical optimization. IEEE Trans. Cybern. 43(6), 2202–2215 (2013)CrossRef Cai, Y., Wang, J.: Differential evolution with neighborhood and direction information for numerical optimization. IEEE Trans. Cybern. 43(6), 2202–2215 (2013)CrossRef
11.
Zurück zum Zitat Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)MathSciNetCrossRef Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)MathSciNetCrossRef
12.
Zurück zum Zitat Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRef Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRef
13.
Zurück zum Zitat Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for CEC 2014 special session and competition on single objective real-parameter numerical optimiation. Technical Report, Nanyang Technological University, Singapore, Zhenzhou University, China, December 2013. http://www.ntu.edu.sg/home/epnsugan/ Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for CEC 2014 special session and competition on single objective real-parameter numerical optimiation. Technical Report, Nanyang Technological University, Singapore, Zhenzhou University, China, December 2013. http://​www.​ntu.​edu.​sg/​home/​epnsugan/​
Metadaten
Titel
Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method
verfasst von
Laizhong Cui
Genghui Li
Li Li
Qiuzhen Lin
Jianyong Chen
Nan Lu
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-26532-2_70