Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Multi-objective Cooperative Coevolutionary Algorithm with Dynamic Species-Size Strategy Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Rotation Invariance and Rotated Problems: An Experimental Study on Differential Evolution

verfasst von : Fabio Caraffini, Ferrante Neri

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper presents an experimental study on the efficacy of a rotation-invariant Differential Evolution (based on current-to-rand mutation) on a benchmark of test problems in its non-rotated and rotated version. Numerical results show that standard Differential Evolution outperforms rotation-invariant Differential Evolution on the benchmark under consideration for both non-rotated and rotated problems. In other words, the rotation-invariant Differential Evolution does not seem to be more efficient than its standard counterpart to address rotated problems. According to our interpretation, these experimental results show that rotated problems are simply different problems with respect to the non-rotated problems. Furthermore, rotation-invariant Differential Evolution is characterised by its moving operator: it generates an offspring by perturbing all the design variables of a candidate solution at the same time. This logic does not appear to guarantee a better performance on rotated problems.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs
Nächstes Kapitel Multi-strategy Differential Evolution
Literatur
1.
2.
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)CrossRef
3.
4.
Garcia, S., Fernandez, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft. Comput. 13(10), 959–977 (2008)CrossRef
5.
Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)MATH
6.
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report 2005005, Nanyang Technological University and KanGAL, Singapore and IIT Kanpur, India (2005)
7.
Hansen, N., Auger, A., Finck, S., Ros, R., et al.: Real-parameter black-box optimization benchmarking 2010: noiseless functions definitions. Technical report, RR-6829, INRIA (2010)
8.
Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernáindez-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report, 201212, Zhengzhou University and Nanyang Technological University, Zhengzhou China and Singapore (2013)
9.
Lozano, M., Molina, D., Herrera, F.: Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft. Comput. 15(11), 2085–2087 (2011)CrossRef
10.
Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. IEEE Trans. Syst. Man Cybern. part B 37(1), 28–41 (2007)CrossRef
11.
Neri, F., Tirronen, V.: Recent advances in differential evolution: a review and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)CrossRef
12.
Poikolainen, I., Neri, F., Caraffini, F.: Cluster-based population initialization for differential evolution frameworks. Inf. Sci. 297(Supplement C), 216–235 (2015)CrossRef
13.
Lampinen, J., Zelinka, I.: Mechanical engineering design optimization by differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 127–146. McGraw-Hill (1999)
14.
Takahama, T., Sakai, S.: Solving nonlinear optimization problems by differential evolution with a rotation-invariant crossover operation using Gram-Schmidt process. In: Proceedings of the World Congress on Nature and Biologically Inspired Computing, pp. 533–540 (2010)
15.
Anik, T.A., Noman, A.S.M., Ahmed, S.: Preserving rotation invariant properties in differential evolution algorithm. In: 2013 2nd International Conference on Advances in Electrical Engineering (ICAEE), pp. 235–240 (2013)
16.
Bujok, P., Tvrdík, J., Poláková, R.: Differential evolution with rotation-invariant mutation and competing-strategies adaptation. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2253–2258, July 2014
17.
18.
Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (2013)
19.
Liang, J., Qu, B., Suganthan, P., Chen, Q.: Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (2014)
20.
Das, S., Suganthan, P.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRef
21.
Das, S., Mullick, S.S., Suganthan, P.: Recent advances in differential evolution - an updated survey. Swarm Evol. Comput. 27(Supplement C), 1–30 (2016)CrossRef
22.
23.
24.
Zhao, S.Z., Suganthan, P.N., Das, S.: Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Comput. 15(11), 2175–2185 (2011)CrossRef
25.
Suganthan, P.N., Das, S., Mukherjee, S., Chatterjee, S.: Adaptation methods in differential evolution: A review. In: 20th International Conference on Soft Computing MENDEL (2014)
26.
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRef
27.
Iacca, G., Caraffini, F., Neri, F.: Multi-strategy coevolving aging particle optimization. Int. J. Neural Syst. 24(01), 1450008 (2014)CrossRef
28.
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. TR-95-012 (1995)
29.
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. J. Glob. Optim. 11(TR–95–012), 341–359 (1997)CrossRefMATH
30.
Zaharie, D.: Critical values for control parameters of differential evolution algorithm. In: Matuŝek, R., Oŝmera, P. (eds.) Proceedings of 8th International Mendel Conference on Soft Computing, pp. 62–67 (2002)
31.
Weber, M., Neri, F., Tirronen, V.: A study on scale factor in distributed differential evolution. Inf. Sci. 181(12), 2488–2511 (2011)CrossRef
32.
Price, K.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill (1999)
33.
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)CrossRef
34.
Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)MathSciNetMATH
Metadaten
Titel
Rotation Invariance and Rotated Problems: An Experimental Study on Differential Evolution
verfasst von
Fabio Caraffini
Ferrante Neri
Copyright-Jahr
2018
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-77537-1

Electronic ISBN: 978-3-319-77538-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77538-8_41

Premium Partner

    Bildnachweise