A Survey of Intelligence Optimization Algorithm with Thermodynamics

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There are many similarities between the intelligence algorithms and the Statistical Physics and Thermodynamics in many aspects, such as the research object, tasks and methods. Firstly, this paper presents the hybrid intelligence algorithms improved by Thermodynamics. Then the theory analysis of intelligence algorithms by Thermodynamics is presented. Finally, a new research direction, that is the novel intelligence algorithm based on statistical physics and thermodynamics, is proposed for the future.

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386-390

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Dayou Wu. Thermodynamics, kinetic theory of gases and statistical mechanics [M]. Beijing: Science Press, (1983).

Google Scholar

[2] S. Kirkpatrick, C. D. Gelatt, Jr., M. P. Vecchi. Optimization by Simulated Annealing [J]. Science, 1983, 220(4598): 671-680.

DOI: 10.1126/science.220.4598.671

Google Scholar

[3] Mori N, Yoshida J, Tamaki H, Kita H, et al. A thermodynamical selection rule for the genetic algorithm [C]. IEEE Conf. on Evolutionary Computation. New Jersey: IEEE Press, 1995. 188-192.

DOI: 10.1109/icec.1995.489142

Google Scholar

[4] W. Ying, Y. Li, SHEU Phillip C-Y et al. Geometric Thermodynamical Selection for Evolutionary Multi-Objective Optimization [J]. Chinese Journal of Computers. 2010 33(4) 755-767.

DOI: 10.3724/sp.j.1016.2010.00755

Google Scholar

[5] K. Li, Y. Li, L. Kang, et al. A Multi-objective Evolutionary Algorithm Based on Transportation Theory [J]. Chinese Journal of Computers. 2007 30(5) 796-805.

Google Scholar

[6] Wenyong Dong, Dengyi Zhang, Zhong Weicheng et al. The Simulation Optimization Algorithm Based on the ITO process [C]. Third International Conference on Intelligence Computing, ICIC 2007, Qingdao, China, 2007: 115-124.

Google Scholar

[7] Xiaofeng Xie, Wenjun Zhang, Zhilian Yang. A Dissipative Particle Swarm Optimization [C]. Proceedings of the Congress on Evolutionary Computation. Honolulu, HI, USA, 2002: 1456-1461.

Google Scholar

[8] Qing Chang, Minxian Zhong. An improvement of two dimensional threshold segmentation algorithm for infrared image [J]. Journal of East China University of Science and Technology(Natural Science Edition). 2005 31(5) 639-643.

Google Scholar

[9] Lovbjerg M, Krink T. Extending Particle Swarm Optimisers with Self-organized Criticality[C]. Proceedings of the IEEE International Conference on Evolutionary Computation. Hawaii, USA: IEEE Press, 2002, 1588-1593.

DOI: 10.1109/cec.2002.1004479

Google Scholar

[10] Xianbin Cao, Jianguo Duan. Evolutionary Algorithm Based on Immune Selection and SOC Mutation [J]. Journal of System Simulation. 2004 16(8) 1785-1788.

Google Scholar

[11] Adam Prugel-Bennett and Jonathan L. Shapiro. Analysis of genetic algorithms using statistical mechanics [J]. Physical Review Letters. 1994 72 1305-1309.

DOI: 10.1103/physrevlett.72.1305

Google Scholar

[12] J. Shapiro, Adam Prugel-Bennett. Maximum Entropy Analysis of Genetic Algorithm Operators [C]. AISB Workshop on Evolutionary Computing. Berlin: Springer, 1995. 14-24.

DOI: 10.1007/3-540-60469-3_21

Google Scholar

[13] Lars Magnus Rattray. The Dynamics of a Genetic Algorithm under Stabilizing Selection [J]. Complex Systems. 1995 9(3) 213-234.

Google Scholar

[14] Lars Magnus Rattray. Modelling the Dynamics of Genetic Algorithms using Statistical Mechanics [D]. Phd Thesis, Manchester, UK: University of Manchester, (1996).

Google Scholar

[15] Alex Rogers and Adam Prugel-Bennett. Modelling the Dynamics of a Steady State Genetic Algorithm[C]. FOGA, 1998. 57-68.

Google Scholar

[16] Alex Rogers and Adam Prugel-Bennett. Genetic drift in genetic algorithm selection schemes [J]. IEEE Trans. Evolutionary Computation. 1999 3(4) 298-303.

DOI: 10.1109/4235.797972

Google Scholar

[17] Said M. Mikki and Ahmed A. Kishk. Particle Swarm Optimization: A Physics-Based Approach [J]. Synthesis Lectures on Computational Electromagnetics. 2008 3(1) 1-103.

DOI: 10.2200/s00110ed1v01y200804cem020

Google Scholar

[18] Benjun Guo, Jian Huang, Dongdong Chen et al. Intelligent algorithms analysis and judgments based on thermodynamics [J]. Microelectronics & Computer. 2010 27(12) 74-77.

Google Scholar

[19] Marc Mezard. Physics/Computer Science: Passing Messages between Disciplines [J]. Science, 2003, 301(5640): 1685-1686.

DOI: 10.1126/science.1086309

Google Scholar

[20] Wenxiang Gu, Ping Huang, Lei Zhu et al. Research of Phase Transition in Artificial Intelligence [J]. Computer Science. 2011 38(5) 1-7.

Google Scholar

[21] Alex Rogers and Adam Prugel-Bennett and Nicholas R. Jennings. Phase transitions and symmetry breaking in genetic algorithms with crossover [J]. Computer and Information Science, 2006, 358(1): 121-141.

DOI: 10.1016/j.tcs.2006.04.010

Google Scholar

[22] K. Xu, W. Li. Phase transition of SAT problem [J]. China Science(E Series). 1999 29(4) 67-73.

Google Scholar

[23] Christian Borgs, Jennifer Chayes, Boris Pittel. Phase Transition and Finite-size Scaling for the Integer Partitioning Problem [J]. Random Structures and Algorithms, 2001 19(3-4) 247-288.

DOI: 10.1002/rsa.10004

Google Scholar

[24] Katharina A. Zweig, Gergely Palla, Tamás Vicsek. What makes a phase transition? Analysis of the random satisfiability problem [J]. Physica A: Statistical Mechanics and its Applications, 2010 389(8) 1501-1511.

DOI: 10.1016/j.physa.2009.12.051

Google Scholar