MO Hongwei.Research advance on natural computing[J].CAAI Transactions on Intelligent Systems,2011,6(06):544-555.





Research advance on natural computing
哈尔滨工程大学 自动化学院,黑龙江 哈尔滨150001
MO Hongwei
College of Automation, Harbin Engineering University, Harbin 150001, China
natural computing biologyinspired computing swarm intelligence molecular computing
Natural computing is one of the important research areas in the field of computer science and artificial intelligence. It is a new research field which involves many disciplines following development spanning several decades. The aim of natural computing is to seek for the solution to difficult problems faced by humans from nature. Natural computing focused on evolution computing, artificial neural networks, and fuzzy systems in its early days. Over the last two decades, several new natural computing methods, such as swarm intelligence, artificial immune systems, and DNA computing have been proposed. In this paper, it presents research situations, development tendencies, and other matters surrounding new methods such as swarm intelligence were analyzed. Areas of future emphasis and direction in development were also pointed out.


[1]GONG Maoguo, JIAO Licheng, DU Haifeng, et al. Multiobjective immune algorithm with nondominated neighborbased selection[J]. Evo Comput, 2008, 16(2): 225255.
[2]CHEN Tianhi, TANG Ke, CHEN Guoliang, et al. Analysis of computational time of simple estimation of distribution algorithms[J]. IEEE Trans on Evolutionary Computation, 2010, 14(1): 122.
[3]WANG Y. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Trans on Evolutionary Computation, 2011, 15(1): 5567.
[4]CHEN Weineng, ZHANG Jun, CHUNG H S H, et al. A novel setbased particle swarm optimization method for discrete optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(2): 278300.
[5]崔逊学. 多目标进化算法及其应用[M]. 北京: 国防工业出版社, 2006: 110.
[6]郑金华. 多目标进化算法及其应用[M]. 北京: 科学出版社, 2007: 110.
[7]中国科学技术协会.智能科学与技术学科发展报告[R].北京:中国科学技术出版社, 2010.
[8]马义德, 李廉, 王亚馥, 等. 脉冲耦合神经网络原理及其应用[M]. 北京: 科学出版社, 2006: 125.
[9] ZHOU Z H, WU J X, JIANG Y, et al. Genetic algorithm based selective neural network ensemble[C]//Proc of the 17th International Joint Conference on Artificial Intelligence (IJCAI’01). Seattle, USA, 2001, 2: 797802. 
[10]史忠植. 神经网络[M]. 北京: 高等教育出版社, 2009: 1205.
[11]LIU Y M, CHEN G Q, YING M S. Fuzzy logic, soft computing and computational intelligence[M]. Berlin: SpringerVerlag, 2005: 110.
[12]DORIGO M, MANIEZZO V, COLORNI A. The ant system: optimization by a colony of cooperating agents[J]. IEEE Trans Sys, Man, and Cybernetics,1996, 26(1): 113.
[13]KENNEDY J, EBERHART R. Particle swarm optimization[C]//IEEE Int Conf on Neural Networks. Piscataway, USA, 1995: 19421948.
[14]王磊, 潘进, 焦李成. 基于免疫策略的进化算法[J].自然科学进展, 2000, 10(5): 451455.
 WANG Lei, PAN Jin, JIAO Licheng. Evolutionary algorithm based on immune strategy[J]. Progress of Nature Science, 2000, 10(5): 451455.
[15] HUANG S J. An immunebased optimization method to capacitor placement in a radial distribution system[J]. IEEE Trans on Power Delivery, 2000(15): 744749.
[16]DURHAM W. Coevolution: genes, culture, and human diversity[M]. Palo Alto, USA: Stanford University Press, 1994: 3545.
[17]ADLEMAN L M. Molecular computation of solutions to combinatorial problems[J]. Science, 1994, 226(11): 10211024.
[18] PAUN A, PAUN G. The power of communication: p systems with symport/antiport[J]. New Generation Computing, 2002, 20(3): 295305.
[19] PAUN G. Membrane computing: an introduction[M]. Berlin: SpringerVerlag, 2002: 110.
[20] ONG Y S, LIM M H, CHEN X S. Memetic computation:past, present & future[J]. IEEE Computational Intelligence Magazine, 2010(5): 2432.
[21]SHI Y, EBERHART R. Evolutionary computation proceedings[C]//IEEE World Congress on Compu Intel. New York, USA, 1998: 6973.
[22]莫宏伟,左兴权,毕晓君.人工免疫系统研究进展[J].智能系统学报, 2009, 4(1): 2329.
MO Hongwei, ZUO Xingquan, BI Xiaojun. Research on development of artificial immune systems[J].CAAI Transations on Intelligent Systems, 2009, 4(1): 2329.
[23]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式鱼群算法[J].系统工程理论与实践, 2002, 22(11): 3238.
LI Xiaolei, SHAO Zhijiang, QIAN Jixin. A fish school optimization algorithm based on animal autonomous[J]. Theory and Practice of System Engineering, 2002, 22(11): 3238.
[24] BASTOS F, CARMELO J A, LIMA N, De FERNANDO B. A novel search algorithm based on fish school behavior[C]//2008 IEEE Int Conf on Systems, Man, and Cybernetics(SMC 2008). Singapore, 2002, 22(11): 3238.
[25] MELLER S, MARCHETTO J, AIRAGHI S, KOUMOUTSAKOS P. Optimization based on bacterial chemotaxis[J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 1629.
[26] TERESHKO V. Reactiondiffusion model of a honeybee colony’s foraging behaviour[J]. Parallel Problem Solving from Nature, 2000,Computer Science, 2000, 1917: 807816.
[27]SIMON D. Biogeographybased optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702713.
[28] DAI Chaohua, ZHU Yufeng, CHEN W R. Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization[J]. Journal of Systems Engineering and Electronics, 2011, 21(2): 300311.
[29]YANG Yan, ZHOU Yongquan, GONG Qiaoqiao. Hybrid artificial glowworm swarm optimization algorithm for solving system of nonlinear equations[J]. Journal of Computational Information Systems, 2010, 6(10): 34313438.
[30]MEHRABIAN A R, LUCAS C. A novel numerical optimization algorithm inspired from weed colonization[J]. Ecological Informatics, 2006(1): 355366.
[31]YANG Shuyuan, WANG Min, JIAO Licheng. Quantum particle swarm optimization[C]//Proc of IEEE Congress on Evolution Computation. Washington, DC, USA, 2004: 320324.
[32]YUCHI M, KIM J H. Ecologyinspired evolutionary algorithm using feasibilitybased grouping for constrained optimization[C]//Proc of the IEEE Congress on Evolutionary Computation. Edinburgh, UK, 2005: 14551461.
[33]JADERICK P P, MICHAEL J M, MENDOZA M,et al. Solving symmetric and asymmetric TSPs by artificial chemistry[C]//Philippine Computing Science Congress. Philippine, 2004: 17.
[34]PATON R. Computing with biological metaphors[M]. London: Chapman & Hall, 2001: 15.
[35]KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598): 671680.
[36]SHOR P W. Algorithm for quantum computation:discrete logarithms and factoring[C]//Proc of 35th Annual Symposium on Foundations of Computer Science. New Mexico, USA: IEEE Computer Society Press, 1994: 124134.
[37]TAYARANI M H N, AKBARZADEH M R T. Magnetic optimization algorithms a new synthesis[C]//IEEE Congress on Evolutionary Computation. Hong Kong, China, 2008: 26592665.
[38]De CASTRO L N. Fundamentals of natural computing[M]. Champman & Hall/CRC. Florida, USA, 2006: 320.
[39] BONABEAU E, DORIGO M, THERAULAZ G. Swarm intelligence: from natural to artificial systems[M]. New York, USA: Oxford University Press, 1999: 215.
[40]MELLER S, MARCHETTO J, AIRAGHI S, KOUMOUTSAKOS P. Optimization based on bacterial chemotaxis[J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 1629.
[41]PASSINO K M. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Syst Mag, 2002, 22(3): 5267.
[42]TANG W J, WU Q H, SAUNDERS J R. A novel model for bacteria foraging in varying environments[C]//Proc ICCSA. Berlin, SpringerVerlag, 2006: 556565.
[43]ACHARYA D P, PANDA G, MISHRA S,et al. Bacteria foraging based independent component analysis[C]//Proc Int Conf Comput Intell Multimedia Applicat. Piscataway, USA: IEEE Press, 2007: 527531.
[44]DASGUPTA S, ABRAHAM D A. Adaptive computational chemotaxis in bacterial foraging optimization: an analysis[J]. IEEE Tran on Evo Comput, 2009, 13(4): 919942.
[45]KIM D H, ABRAHAM A, CHO J H. A hybrid genetic algorithm and bacterial foraging approach for global optimization[J]. Inform Sci, 2007, 177(18): 39183937.
[46]MISHRA S. A hybrid least squarefuzzy bacterial foraging strategy for harmonic estimation[J]. IEEE Trans Evol Comput, 2005, 9(1): 6173.
[47]BISWAS A, DASGUPTA S, DAS S, ABRAHAM A. Synergy of PSO and bacterial foraging optimization: a comparative study on numerical benchmarks[C]//Proc 2nd Int Symp Hybrid Artificial Intell Syst (HAIS)Advances Soft Computing Ser. [S.l.], SpringerVerlag, ASC, 2007: 255263.
[48]PASSINO K M. Biomimiery of bacterial foraging for distributed optimization and control[J]. IEEE Control System Magazine, 2002(6): 5267.
[49]MAJHI R, PANDA G, SAHOO G. Efficient prediction of stock market indices using adaptive bacterial foraging optimization(ABFO)and BFO based techniques[J]. Expert Systems with Applications, 2009, 36 (6): 1009710104.
[50]LI M S, TANG W J, TANG W H, et al. Bacteria foraging algorithm with varying population for optimal power fow[C]//Proc Applications of Evolutionary Computing 2007. Berlin, SpringerVerlag, 2007: 3241.
[51]MO Hongwei, YIN Yujing. Image segmentation based on bacterial foraging and FCM algorithm[J]. International Journal of Swarm Intelligence Research, 2011, 2(3): 1629. 
 [52]李威武,王慧,邹志君,等.基于细菌群体趋药性的函数优化方法[J].电路与系统学报, 2005, 10(1): 5863.
 LI Weiwu, WANG Hui, ZOU Zhijun, et al. Function optimization based on bacterial chemotaxis[J]. Journal of Electrical Circuit and System, 2005, 10(1): 5863.
[53]吕慧显. 基于微细菌群体趋药性的函数优化算法[J]. 青岛大学学报:工程技术版, 2009, 24(1): 1926.
Lv Huixian. Function optimization based on micro bacterial chemotaxis[J]. Journal of Qingdao University: Engineering, 2009, 24(1): 1926.
[54]曹黎侠,张建科.细菌趋药性算法理论及应用研究进展[J]. 计算机工程与应用, 2006, 42(1): 4446.
CAO Lixia, ZHANG Jianke. Research development of theory and application of bacterial chemotaxis algorithm[J]. Computer Engineering and Application, 2006, 42(1): 4446.
[55]张煜东,吴乐南.多态细菌趋药性优化[J]. 计算机工程与应用, 2009, 45(18): 611.
ZHANG Yudong, WU Lenan. Multimodal bacterial chemotaxis optimization[J]. Computer Engineering and Application, 2009, 45(18): 611.
[56]TERESHKO V. Reactiondiffusion model of a honeybee colony’s foraging behaviour[M]. Berlin:SpringerVerlag, 2000: 807816.
[57]TERESHKO V, LEE T. How information mapping patterns determine foraging behaviour of a honeybee colony[J]. Open Systems and Information Dynamics, 2002(9): 181193.
[58]TERESHKO V, LOENGAROV A. Collective decisionmaking in honeybee foraging dynamics[J]. Computing and Information systems Journal, 2005, 9(3): 17.
[59]TEODOROVIC D. Transport modeling by multiagent systems: a swarm intelligence approach[J]. Transportation Planning and Technology, 2003, 26(4): 289312.
[60]LUCIC P, TEODOROVIC D. Transportation modeling: an artificial life approach[C]//ICTAI, Washington, DC, USA, 2002: 216223.
[61]PHAM D T, GHANBARZADEH A, KOC E, et al. The bees algorithm—a novel tool for complex optimisation problems[C]//Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006). Cardiff, UK: Elsevier, 2006: 454459.
[62]DRIAS H, SADEG S, YAHI S. Cooperative bees swarm for solving the maximum weighted satisfiability problem, computational intelligence and bioinspired systems[C]//8th International Workshop on Artificial Neural Networks (IWANN 2005). Vilanova, Barcelona, Spain, 2005: 810.
[63]BENATCHBA K, ADMANE L, KOUDIL M. Using bees to solve a datamining problem expressed as a maxsat one[C]//First International WorkConference on the Interplay Between Natural and Artificial Computation (IWINAC 2005). Palmas, Canary Islands, Spain, 2005: 1518.
[64]WEDDE H F, FAROOQ M, ZHANG Y. Beehive: an efficient faulttolerant routing algorithm inspired by honeybee behavior, ant colony, optimization and swarm intelligence[C]//4th International Workshop ANTS 2004. Brussels, Belgium, 2004: 58.
[65]YANG X S. Engineering optimizations via natureinspired virtual bee algorithms[C]//Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, Lecture Notes in Computer Science.Berlin/Heidelberg: SpringerVerlag. 2005, 3562: 317323.
[66]PHAM D T, GHANBARZADEH A, KOC E, et al. The bees algorithm[R].[S.l.], Manufacturing Engineering Centre, Cardiff University, 2005.
[67]KARABOGA D. An idea based on honeybee swarm for numerical optimization TR06[R]. [S.l.], Computer Engineering Department, Engineering Faculty, Erciyes University, 2005.
[68]BASTURK B, KARABOGA D. An artificial bee colony (ABC) algorithm for numeric function optimization[C]//IEEE Swarm Intelligence Symposium 2006. Indianapolis, USA, 2006: 4550.
[69]KARABOGA D, BASTURK B A. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization, 2007, 39(3): 459471.
[70]KARABOGA D, BASTURK B. On the performance of artificial bee colony (abc) algorithm[J]. Applied Soft Computing, 2008, 8(1): 687697.
[71]〖JP3〗KARABOGA D, BASTURK B. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems[M]. Berlin:SpringerVerlag, 2007: 789798.
[72]KARABOGA D, AKAY B B, OZTURK C. Artificial bee colony (ABC) optimization algorithm for training feedforward neural networks[C]//Modeling Decisions for Artificial Intelligence. Berlin: SpringerVerlag, 2007: 318329.
[73]KARABOGA D, AKAY B B. An artificial bee colony (ABC) algorithm on training artificial neural networks[C]//15th IEEE Signal Processing and Communications Applications. Eskisehir, Turkey, 2007: 14.
[74]KARABOGA N. A new design method based on artificial bee colony algorithm for digital IIR filters[J]. Journal of The Franklin Institute, 2009, 346 (4): 328348.
[75]ALOK S. An artificial bee colony algorithm for the leafconstrained minimum spanning tree problem[J]. Applied Soft Computing, 2009, 9(2): 625631.
[76]DERVIS K, BAHRIYE A. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009(214): 108132.
[77]ERGEZER M, SIMON D, DU Dawei. Oppositional biogeographybased optimization[J]. Journal of Systems, Man, and Cybernetics, 2009, 39(5): 10351040.
[78]MA Haiping. An analysis of the behavior of migration models for biogeographybased optimization[J]. Information Sciences, 2010, 180(18): 34443464.
[79]GONG Wenyin, CAI Zhihua, LING Charlexin,et al. A realcoded biogeographybased optimization with neighborhood search operator[J]. Applied Mathematics and Computation, 2010, 216(9): 27492758.
[80]DU D W, SIMON D, ERGEZER M. Biogeographybased optimization combined with evolutionary strategy and immigration refusal[C]//Proc of the IEEE Conference on Systems, Man, and Cybernetics. SanAntonio, Texas,2005: 10231028. 
[81]GONG Wenyin, CAI Zhihua, LING Ccharlexin. DE/BBO: a hybrid differential evolution with biogeographybased optimization for global numerical optimization[J].Soft Computing, 2011, 5(4): 645665.
[82]MA H, NI S, SUN M. Equilibrium species counts and migration model tradeoffs for biogeographybased optimization[C]//Proc of the IEEE Conference on Decision and Control. Shanghai, China, 2009: 33063310.
[83]SIMON D. A probabilistic analysis of a simplified biogeographybased optimization algorithm[EB/OL].[20090211]. http: //academic.csuohio.edu/simond/bbo/ simplified. 
[84] SIMON D, ERGEZER M, DU D. Population distributions in biogeographybased optimization algorithms with elitism[C]//Proc of the IEEE Conference on Systems, Man, and Cybernetics. San Antonio, USA, 2009: 10171022.
[85] SINGH U, KUMAR H, KAMAL T S. Linear array synthesis using biogeography based optimization[J]. Progress in Electromagnetics Research, 2010, 11: 2537. 
[86]TAN Lixiang, GUO Li. Quantum and biogeography based optimization for a class of combinatorial optimization[C]//GEC’09.[S.l.], 2009: 969972.
[87]NAVDEEP K, JOHAL S, KUNDRA S H. A hybrid FPAB/BBO algorithm for satellite image classification[J]. International Journal of Computer Applications, 2010, 6(5): 3136.
[88]ANIRUDDHA B, CHATTOPADHYAY P K. Solving complex economic load dispatch problems using biogeographybased optimization[J]. Expert Systems with Applications, 2010, 37(5): 36053615. 
[89]MO Hongwei, XU Lifang. Biogeography migration algorithm for traveling salesman problem[J]. International Journal of Intelligent Computing and Cybernetics, 2011, 4(3): 311330.
[90]PAN Yongxin, LIN Wei, LI Jinhua, et al. Reduced efficiency of magnetotaxis in magnetotactic coccoid bacteria in higher than geomagnetic fields[J]. Biophysical Journal, 2009, 97: 986991.
[91]PAUN G, ROZENBERG G, SALOMAA A. DNA computing:new computing paradigms[M]. Berlin: SpringerVerlag, 1998: 112.
[92]FRANCOA G, MARGENSTERN M. A DNA computing inspired computational model[J]. Theoretical Computer Science, 2008(404): 8896.
[93]RAMAKRISHNAN N, BHALLA U S, TYSON J J. Computing with proteins[J]. Computer, 2009, 42(1): 4756.
[94]TRINCA?D, RAJASEKARAN S. Coping with diffraction effects in proteinbased computing through a specialized approximation algorithm with constant overhead[C]//2010 10th IEEE Conference on Nanotechnology (IEEENANO).Seoul, Korea, 2010: 802805.
[95]PANCHENKOA, PRZYTYCKA T. Proteinprotein interactions & networks[M]. Computing Methods for Identification, Analysis & Prediction. Berlin: Springer, 2010: 610. 
[96]EICHELBERGER C N, NAJARIAN K. Simulating protein computing: character recognition via probabilistic transition trees[C]//IEEE International Conference on Granular Computing.[S.l.], 2006: 101105.
[97]HENKEL V C, RENO S B, CRINA I A, et al. Protein output for DNA computing[J]. Natural Computing, 2005, 4(1): 110.
[98]ANDY A. Molecular computing:aromatic arithmetic[J]. Nature Physics, 2010, 6: 325326.
[99]HAMEL J S. A thermodynamic turing machine: artificial molecular computing using classical reversible logic Switching networks[EB/OL].[20101125].http://arxiv.org/abs/0904.3273.3273v2, 2009.
[100]PAUN G, ROZENBERG G, SALOMAA A. DNA computingnew computing paradigms[M]. Berlin: SpringerVerlag, 1998: 39.
[101]GARCAQUISMONDO M, GUTIERREZESCUDERO R, PEREZHURTADO I, et al. An overview of PLingua 2.0[J]. Lecture Notes in Computer Science, 2010(5957): 264288.
[102]CHRISTINAL H A, DIAZPERNIL D, REAL P. Segmentation in 2D and 3D image using tissuelike P system[J]. Lecture Notes in Computer Science, 2009(5856): 169176.
[103]ESCUELA G, HINZE T, DITTRICH P, et al. Modelling modified atmosphere packaging for fruits and vegetables using membrane systems[C]//Proc of the Third International Conference on Bioinspired Systems and Signal Processing. Valencia,Spain: INSTICC Press, 2010: 306311.
[104]ZHAO J , WANG N. A bioinspired algorithm based on membrane computing and its application to gasoline blending scheduling[J]. Computers and Chemical Engineering, 2011, 35(2): 272283.
[105]PAUN G. A quick introduction to membrane computing[J]. The Journal of Logic and Algebraic Programming, 2010(79): 291294.
[106]LAM A Y S, LI V O K. Chemicalreactioninspired metaheuristic for optimization[J]. IEEE Trans on Evolutionary Computation, 2010, 14(3): 381400.


 MO Hong-wei,ZUO Xing-quan,BI Xiao-jun.Advances in artificial immune systems[J].CAAI Transactions on Intelligent Systems,2009,4(06):21.


收稿日期: 2011-04-01.
基金项目:国家自然科学基金资助项目(61075113);黑龙江省青年学术骨干项目资助项目(1155G18);中央高校基本科研业务自由探索基金资助项目(HEUCF110441). 
更新日期/Last Update: 2012-02-29