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Application of Hybrid Genetic Algorithm Based on Simulated Annealing in Function Optimization

Authors: Panpan Xu, Shulin Sui, Zongjie Du

Abstract:

Genetic algorithm is widely used in optimization problems for its excellent global search capabilities and highly parallel processing capabilities; but, it converges prematurely and has a poor local optimization capability in actual operation. Simulated annealing algorithm can avoid the search process falling into local optimum. A hybrid genetic algorithm based on simulated annealing is designed by combining the advantages of genetic algorithm and simulated annealing algorithm. The numerical experiment represents the hybrid genetic algorithm can be applied to solve the function optimization problems efficiently.

Keywords: simulated annealing, Genetic Algorithm, function optimization, Hybrid genetic algorithm

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110710

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References:


[1] Holland, J.H. Adaptation in Natural and Artificial Systems. Ann Arbor. University of Michigan press, 1975.
[2] De Jong K. A. Analysis of the Behavior of a Class of Genetic Adaptive Systems .Michigan: University of Michigan, 1975, pp.76-81.
[3] L.Y. Jia, X. Du. Study of Parallel Genetic Algorithm, Journal of Hunan City University, 2006, 15(3), pp.72-74.
[4] Y.J. Huang, Y.X. Wu, H.B. Liu. Application of Improved Genetic Algorithm in TSP, Computer Engineering and Design, 2007, 28(24), pp.9-11.
[5] S.D. Yang, S.P. Li. Study of Genetic Algorithm, Studies in Network and Information, 2008, 27(9), pp.60-62.
[6] J. Liu, Y. Wang. An Efficient Hybrid Genetic Algorithm, Journal of Henan University, 2002, 30(2), pp.49-53.
[7] Y. Zeng. Application of Improved Genetic Algorithm in Nonlinear Equations, Journal of East China Jiaotong University, 2004, 21(4), pp.39-41.
[8] H.G. Chen. Mechanism of Simulated Annealing Algorithm, Journal of Tongji University (Natural Science), 2004, 32(6), pp.802-804.
[9] X.Q. Du, J.X. Cheng. Study of Robot Strategy on Annealing Evolutionary Algorithm, Computer Technology and Development, 2008, 18(2), pp.101-103.
[10] Q. Wang. Improved Simulated Annealing Algorithm and its Application, Applied Mathematics, 1993, 4(3), pp. 392-397.
[11] J.L. Liu. Improved Simulated Annealing Algorithm of Reactive Power Optimization, Journal of Electric Power, 1998, 13(2), pp.86-89.
[12] W.X. Xing, J.X. Xie. Modern optimization methods, Beijing: Tsinghua University Press, 1999, pp.181-182.
[13] K.J. Zhou, D.B. Li. Product Assembly Sequence Planning Based on Genetic Simulated Annealing Algorithm, Computer Integrated Manufacturing Systems, 2006(7).
[14] Q. Yan, Y.L. Bao. New Genetic Simulated Annealing Algorithm in Logistics Distribution Routing Problem, Computer Application, 2004, 24(3), pp. 261-263.
[15] H.M. Jin, L. Ma. Application of Genetic Annealing Evolutionary Algorithm on Knapsack Problem, Journal of Shanghai University of Technology, 2004, 26(6), pp. 561-564.
[16] T.N. Liu, Y.B. Duan, S. Lei. Analysis of A Hybrid Algorithm and Convergence, Automation Techniques and Applications, 2003, 22(10), pp.4-6.
[17] W. Xie, K.L. Fang. Solving for Global Solution in Non-differentiable Nonlinear Function by Hybrid Genetic Algorithm, Control Theory & Applications, 2000, 17(2), pp.180-183.
[18] K. Chen, J.Y. Ma, H.B. Wen. An Improved Integrated Hybrid Algorithm, Statistics and Decision, 2008, 17, pp.41-43.
[19] L. He. Analysis of Hybrid Algorithm Convergence and Estimation of Convergence Rate, Systems Engineering, 1999, 6(17), pp.64-68.