An Improved Differential Evolution Optimization Based on P System

Yan Hua Zhong; Shu Zhi Nie

doi:10.4028/www.scientific.net/AMR.756-759.3346

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759An Improved Differential Evolution Optimization...

An Improved Differential Evolution Optimization Based on P System

Abstract:

Membrane computing (P system) is a new computing model; it comes from the research of the basic function and structure of creatures cell membrane. It has complex structure and multi-level. It converges quickly and has high quality of the optimal results. In this article, discussed the basic theory of the membrane computing and the steps of the algorithm. Apply Membrane computing principle to the differential evolution algorithm, constructed a hybrid differential evolution algorithm on the basis of the membrane structure. Finally, utilized the Objective Functions to test the new algorithm performance, compared with related algorithms to analyze the advantages and disadvantages of the new algorithm.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3346-3350

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3346

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Online since:

September 2013

Authors:

Yan Hua Zhong, Shu Zhi Nie

Keywords:

Cell Membrane Structure, Differential Evolution, Function Optimization, P System

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References

[1] Storn R , Price K. Differential evolution —A simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley: University of California, (2006).

Google Scholar

[2] Lampinen J. A bibliography of differential evolution algorithm [EB/ OL]. (2002210214). Http: / / www. lut . fi/~jlampine/debiblio. htm.

Google Scholar

[3] B. Liu, L. Wang, Y.H. Jin, et al. Advances in particle swarm optimization algorithm. Control and Instrument s in Chemical Industry, 2005, 32 (3): 126.

Google Scholar

[4] Gh. Paun. Computing with membranes. Journal of Computer and System Sciences, 61, 1(2000), 108-143.

Google Scholar

[5] X.F. Xie, W.J. Zhang, G.R. Zhang, et al. Empirical study of differential evolution. Control and Decision, 2004, 19 (1): 49252, 56.

Google Scholar

[6] B. Liu, L. Wang, Y.H. Jin. Advances in differential evolution. Control and Decision. 2008, 22(7), 721-729.

Google Scholar

[7] S.J. Yuan, X.S. Gu. A Differential Evolution Based Membrane Computing Algorithm. Shanghai Institute of Chemistry and Chemical Engineering 2010 Annual Conference Proceedings (automated thematic), (2010).

Google Scholar

[8] J. Liu, J. lampinen. A fuzzy adaptive differential evolution algorithm. Soft Computing. 2005, 9(6): 448-462.

DOI: 10.1007/s00500-004-0363-x

Google Scholar