International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.1, No.3, Sep. 2011

Binary Encoding Differential Evolution for Combinatorial Optimization Problems

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Changshou Deng,Bingyan Zhao,Yanlin Yang,Hai Zhang

Index Terms

Combinatorial optimization problem; Differential Evolution; Binary encoding ; semi-probability mutation operator


Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous field. The operators in the original Differential Evolution are simple; however, these operators make it impossible to use the Differential Evolution in the binary space directly. Based on the analysis of problems led by the mutation operator of the original Differential Evolution in the binary space, a new mutation operator was proposed to enable this optimization technique can be used in binary space. The new mutation operator, which is called semi-probability mutation operator, is a combination of the original mutation operator and a new probability-based defined operator. Initial experimental results of two different combinatorial optimization problems show its effectiveness and validity.

Cite This Paper

Changshou Deng,Bingyan Zhao,Yanlin Yang,Hai Zhang,"Binary Encoding Differential Evolution for Combinatorial Optimization Problems", IJEME, vol.1, no.3, pp.59-66, 2011.


[1]Storn, R. and K. Price, “Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal Global Optimization,1997,11, pp.241-354.

[2]Rogalsky, Derksen, and Kocabiyik, “ Differential evolution in aerodynamic optimization”, Proc. of 46th Annual Conf. of Canadian Aeronautics and Space Institute,1999, pp. 29-36

[3]Das S. and Konar A, “Design of tow dimensional IIR filters with modern search heuristics: a comparative study”, International Journal of Computational Intelligence and Applications, World Scientific Press, 2006,Vol.6 No.3,pp.176-185.

[4]Das S., Abraham A. and Konar A., “Adaptive clustering using improved differential evolution algorithm”, IEEE Transaction on Systems, Man and Cybernetics-Part A, IEEE Press, USA,2008,vol.38, issue 1:pp. 218-237.

[5]Versterstrom J, Thomsen R. “ A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithm on numerical benchmark problems “// Evolutionary Computation, CEC2004.Portland OR: IEEE press, 2004,2: 1980-1987

[6]T. Gong, L. T. Andrew. “Differential Evolution for Binary Encoding”. Soft Computing in Industrial Applications, ASC 39, pp. 251-262, 2007.

[7]Changshou Deng, Bingyan Zhao, Yanling Yang, etc. Novel binary Differential Evolution without scale factor F, Third International Workshop on Advanced Computational Intelligence, 2010,8: 250-253.

[8]Y. Liu and C. Liu.: “A Schema-Guiding Evolutionary Algorithm for 0-1 Knapsack Problem”. Iacsit-sc. In: 2009 International Association of Computer Science and Information Technology –Spring Conference, IEEE Press, Singapore,pp.160-164, 2009.