A Comparative Study of Various Strategies in Differential Evolution

• Autorzy: Boryczka U. , Juszczuk P. , Kłosowicz L.
• Konferencja: Evolutionary Computation and Global Optimization 2009 / National Conference (12 ; 1-3.06.2009 ; Zawoja, Poland)
• Języki publikacji: EN

Abstrakty

EN

This paper presents a comparison of various strategies of differential evolution. Differential evolution (DE) is a simple and powerful optimization method, which is mainly applied to numerical optimization and many other problems (for example: neural network train, filter design or image analysis). The comparison of various modifications (named strategies) of DE algorithm allows to choose the algorithm version which is best adjusted to desirable requirements. Three parameters are tested: speed, accuracy and completeness. The first section of this article presents general optimization problem and says a little about methods used to function optimization. The next section describes differential evolution - basic algorithm is presented. Two different crossover methods, process of initial population creation and basic mutation schema are described. The third section describes the most popular DE strategies. In the fourth section a new modification (called λ-modification) of DE algorithm is presented. Next section provides basic information about four test functions and differential evolution parameters used in research. The paper presents then summary and final conclusions.

Słowa kluczowe

EN

differential evolution; DE; evolutionary algorithm; evolutionary computation; optimization

PL

ewolucja różnicowa; algorytm ewolucyjny; obliczenia ewolucyjne; optymalizacja

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2009
• Tom: z. 169
• Strony: 19--26
• Opis fizyczny: Bibliogr. 13 poz., tab.

Twórcy

autor

Boryczka U.

autor

Juszczuk P.

autor

Kłosowicz L.

• University of Silesia, Institute of Computer Science, Sosnowiec, Poland,

Bibliografia

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• [4] J. Lampinen and I. Zelinka. Mixed variable non-linear optimization by differential evolution. In Zlin, Czech Republic. Technical University of Brno. Faculty of Technology Zlin, Department of Automatic Control, pages 45-55, 1999.
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• [7] K. Price, R. Storn, and J. Lampinen. Differential evolution - A practical Approach to Global Optimization. Springer, 2005.
• [8] R. Storn. Differential evolution design of an iir-filter. In in IEEE International Conference on Evolutionary Computation ICEC96, pages 268-273. IEEE Press, 1996.
• [9] R. Storn. On the usage of differential evolution for function optimization. In NAFIPS'96, pages 519-523. IEEE, 1996.
• [10] R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optimization, 11(4):341-359, 1997.
• [11] J. Tvrdík. Differential evolution: Competitive setting of control parameters. In Proceedings of the international multiconference on computer science and information technology, pages 207-213, 2006.
• [12] Kasemir K.U. and K. Betzler. Detecting ellipses of limited eccentricity in images with high noise levels. Image and Vision Computing, 21:221-227(7), 10 February 2003.
• [13] D. Zaharie. A multipopulation differential evolution algorithm for multimodal optimization. In Proceedings of Mendel 2004, 10th International Conference on Soft Computing, pages 17-22, Brno, Czech Republic, 2004.