自然启发算法库构建设想及其在新材料研发中的意义

doi:10.15541/jim20180214

摘要/Abstract

摘要：

材料基因组工程技术是运用人工智能手段实现新材料按需设计的关键技术, 其中尤为重要的是创新智能算法的开发和应用。本文在总结、分析已有自然启发算法的基础上, 提出建立自然启发算法库(Nature-inspired Algorithms Library, NIAL)的设想; 明确了从不同学科取得算法启发并高通量产生新算法的基本思路; 详细阐述了构建该算法库的基本流程, 并剖析建立自然启发算法库平台的若干优势和特点。最后, 展望了自然启发算法库在新材料研发中的应用模式, 希望借此提升人工智能在材料基因组工程领域的应用水平。

关键词: 自然启发算法, 材料基因组工程, 人工智能, 综述

Abstract:

The technique for Materials Genetic Initiative (MGI) is the key tool for realizing the demand-oriented design of new materials assisted by the artificial intelligence (AI). Accordingly, the development and application of innovative intelligence algorithms are particularly important. Based on the generalization and analyses of the existing nature-inspired algorithms, this work aims at outlining the suggestion to build the nature-inspired algorithms library (NIAL). The potential route in which inspirations are obtained from varieties of disciplines, was used to produce new algorithms in high-throughput ways is introduced. The general procedure for building algorithm library is elaborated, while its advantages and characteristics are anatomized. Finally, the potential of NIAL in new materials development has been envisioned to enhance the standard for the application of AI including MGI.

Key words: Nature-Inspired Algorithms Library, Materials Genetic Initiative, artificial intelligence, review

中图分类号:

TQ174

都时禹, 张一鸣, 罗侃, 黄庆. 自然启发算法库构建设想及其在新材料研发中的意义[J]. 无机材料学报, 2019, 34(1): 27-36.

DU Shi-Yu, ZHANG Yi-Ming, LUO Kan, HUANG Qing. Design of the Nature-inspired Algorithms Library and Its Significance for New Materials Research and Development[J]. Journal of Inorganic Materials, 2019, 34(1): 27-36.

图/表 3

图1 算法构建流程

Fig. 1 Process for algorithms construction

图2 自然启发算法库平台开发流程

Fig. 2 Development process for the nature-inspired algorithms library platform

图3 材料建模的不同方法与算法

Fig. 3 Methods and algorithms for material modeling

参考文献 144

[1]	XIANG X D, SUN X, BRICEÑO G, et al. A combinatorial approach to materials discovery. Science, 1995, 268(5218): 1738.
[2]	ZHU J, HUANG H Y, XIE J X.Recent progress and new ideas for accelerating research in rare earth steel. Journal of Iron and Steel Research, 2017, 29(7): 513-529.
[3]	RAMAKRISHNA S, ZHANG T, LU W, et al.Materials informatics. Journal of Intelligent Manufacturing, 2018(5): 1-20.
[4]	LIU Z, LI Y, SHI D, et al.The development of cladding materials for the accident tolerant fuel system from the Materials Genome Initiative. Scripta Materialia, 2018, 143: 129-136.
[5]	LIN HAI Z J L Y. The development of material genome technology in the field of new energy materials. Energy Storage Science and Technology, 2017, 6(5): 990.
[6]	WHITE A A.Big data are shaping the future of materials science. Mrs Bulletin, 2013, 38(8): 594-595.
[7]	WARD L, AGRAWAL A, CHOUDHARY A, et al. A general- purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2016, 2: 16028-1-7.
[8]	MOUNET N, GIBERTINI M, SCHWALLER P, et al.Two- dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nature Nanotechnology, 2018, 13(3): 246-252.
[9]	XU S, LI X, ZHAO Y, et al.Two-dimensional semiconducting boron monolayers. Journal of the American Chemical Society, 2017, 139(48): 17233-17236.
[10]	TAN T L, JIN H M, SULLIVAN M B, et al.High-throughput survey of ordering configurations in MXene alloys across compositions and temperatures. ACS Nano, 2017, 11(5): 4407-4418.
[11]	ZHOU J, LIN J, HUANG X, et al.A library of atomically thin metal chalcogenides. Nature, 2018, 556(7701): 355.
[12]	LAWSON C L, HANSON R J, KINCAID D R, et al.Basic linear algebra subprograms for Fortran usage. ACM Transactions on Mathematical Software (TOMS), 1979, 5(3): 308-323.
[13]	ANDERSON E, BAI Z, BISCHOF C, et al.LAPACK Users' Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA. Society for Industrial and Applied Mathematics, 1999.
[14]	SANDERSON C, CURTIN R.Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software, 2016.
[15]	INTEL. Intel® Math Kernel Library Developer Reference, 2017.
[16]	DEMMEL J W, HEATH M T, VAN DER VORST H A. Parallel numerical linear algebra. Acta Numerica, 1993, 2: 111-197.
[17]	KETTNER L, N A HER S, GOODMAN J E, et al. Two Computational Geometry Libraries: LEDA and CGAL. Handbook of Discrete and Computational Geometry, Chapman & Hall/CRC, 2004: 1435-1463.
[18]	PULLI K, BAKSHEEV A, KORNYAKOV K, et al.Real time computer vision with OpenCV. Queue, 2012, 10(4): 40.
[19]	CHAKRABORTI N.Genetic algorithms in materials design and processing. International Materials Reviews, 2004, 49(3/4): 246-260.
[20]	PASZKOWICZ W.Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Materials and Manufacturing Processes, 2009, 24(2): 174-197.
[21]	HKDH B.Neural networks in materials science. ISIJ international, 1999, 39(10): 966-979.
[22]	BHADESHIA H.Neural networks and information in materials science. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2009, 1(5): 296-305.
[23]	BHADESHIA H, DIMITRIU R C, FORSIK S, et al.Performance of neural networks in materials science. Materials Science and Technology. 2009, 25(4): 504-510.
[24]	ZHANG Y M, YANG S, EVANS J.Revisiting Hume-Rothery's rules with artificial neural networks. Acta Materialia, 2008, 56(5): 1094-1105.
[25]	ZHANG Y M, EVANS J, YANG S F.Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations. Philosophical Magazine, 2010, 90(33): 4453-4474.
[26]	ZHANG Y, EVANS J R, YANG S.Corrected values for boiling points and enthalpies of vaporization of elements in handbooks. Journal of Chemical and Engineering Data, 2011, 56(2): 328-337.
[27]	ZHANG Y M, UBIC R, XUE D F, et al.Predicting the structural stability and formability of ABO3-type perovskite compounds using artificial neural networks. Materials Focus, 2012, 1(1): 57-64.
[28]	NADEAU R, CLOUTIER E, GUAY J.New evidence about the existence of a bandwagon effect in the opinion formation process. International Political Science Review, 1993, 14(2): 203-213.
[29]	EARMAN J, MOSTERIN J.A critical look at inflationary cosmology. Philosophy of Science, 1999, 66(1): 1-49.
[30]	TRIMBLE V.Existence and nature of dark matter in the universe. Annual Review of Astronomy & Astrophysics, 1987, 25(1): 425-472.
[31]	EINSTEIN A, PODOLSKY B, ROSEN N.Can quantum- mechanical description of physical reality be considered complete? Phys. Rev., 1935, 47: 777-780.
[32]	SHANNON C E.A mathematical theory of communication. The Bell System Technical Journal, 1948, 27(3): 379-423.
[33]	YANG X.A New Metaheuristic Bat-inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, 2010: 65-74.
[34]	KHAN K, SAHAI A.A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. International Journal of Intelligent Systems and Applications, 2012, 4(7): 23.
[35]	BEKDA C S G, NIGDELI S M, YANG X. A novel bat algorithm based optimum tuning of mass dampers for improving the seismic safety of structures. Engineering Structures, 2018, 159: 89-98.
[36]	KHACHATURYAN A, SEMENOVSKAYA S, VAINSTEIN B.Statistical-thermodynamic approach to determination of structure amplitude phases. Sov. Phys. Crystallography, 1979, 24(5): 519-524.
[37]	KHACHATURYAN A, SEMENOVSOVSKAYA S, VAINSHTEIN B.The thermodynamic approach to the structure analysis of crystals. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 1981, 37(5): 742-754.
[38]	KIRKPATRICK S, GELATT C D, VECCHI M P.Optimization by simulated annealing. Science, 1983, 220(4598): 671-680.
[39]	ANDERSON H L, METROPOLIS. Monte Carlo and the Maniac. Los alamos Science, 1986, 14(14): 96-108.
[40]	BABAI L A S O. Monte-Carlo Algorithms in Graph Isomorphism Testing. Université tde Montréal Technical Report, DMS, 1979.
[41]	LEVIN L A.The tale of one-way functions. Problems of Information Transmission, 2003, 39(1): 92-103.
[42]	GRUNDY D.Concepts and Calculation in Cryptography. Citeseer, 2008.
[43]	QUINLAN J R.Induction of decision trees. Machine Learning, 1986, 1(1): 81-106.
[44]	QUINLAN J R.C4.5: Programs for Machine Learning. Elsevier, 2014.
[45]	COULOM R E M. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. Springer, 2006: 72-83.
[46]	KOCSIS L, SZEPESV A RI C. Bandit Based Monte-Carlo Planning. Springer, 2006: 282-293.
[47]	SILVER D, HUANG A, MADDISON C J, et al.Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529(7587): 484-489.
[48]	SILVER D, SCHRITTWIESER J, SIMONYAN K, et al.Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354.
[49]	LIU Y H, ZHANG W, FAN L.Ecological Pyramid Particle Swarm Optimization. Computer Science, 2017, 44(10): 237-244.
[50]	RAO R V, SAVSANI V J, VAKHARIA D P.Teaching- learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 2011, 43(3): 303-315.
[51]	RAO R V, SAVSANI V J, VAKHARIA D P.Teaching- learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 2012, 183(1): 1-15.
[52]	TUO S, YONG L, DENG F.Survey of teaching-learning-based optimization algorithms. Application Research of Computers, 2013, 30(7): 1933-1938.
[53]	BI X, WANG J.Teaching-learning-based optimization algorithm with hybrid learning strategy. Journal of Zhejiang University (Engineering Science), 2017, 51(5): 1024-1031.
[54]	ZHANG J, LIU K, TAN Y, et al.Random Black Hole Particle Swarm Optimization and Its Application. IEEE, 2008: 359-365.
[55]	HATAMLOU A.Black hole: a new heuristic optimization approach for data clustering. Information Sciences, 2013, 222: 175-184.
[56]	WARNANA D D, OTHERS. Black hole algorithm for determining model parameter in self-potential data. Journal of Applied Geophysics, 2018, 148: 189-200.
[57]	MA L, ZHU Y, LIU Y, et al.A novel bionic algorithm inspired by plant root foraging behaviors. Applied Soft Computing, 2015, 37: 95-113.
[58]	DAN S.Biogeography-based optimization. IEEE Transactions on Evolutionary Computation. 2008, 12(6): 702-713.
[59]	WESCHE T, GOERTLER C, HUBERT W.Modified habitat suitability index model for brown trout in Southeastern Wyoming. North American Journal of Fisheries Management, 1987, 7(2): 232-237.
[60]	WANG C, WANG N, DUAN X, et al.Survey of Biogeography- based Optimization. Computer Science, 2010, 37(7): 34-38.
[61]	MA H, SIMON D, SIARRY P, et al.Biogeography-based optimization: a 10-year review. IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 1(5): 391-407.
[62]	BENIOFF P.The computer as a physical system: a microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines. Journal of Statistical Physics, 1980, 22(5): 563-591.
[63]	FEYNMAN R P.Simulating physics with computers. International Journal of Theoretical Physics, 1982, 21(6/7): 467-488.
[64]	DEUTSCH D.Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. R. Soc. Lond. A, 1985, 400(1818): 97-117.
[65]	JOHNSON M W, AMIN M H, GILDERT S, et al.Quantum annealing with manufactured spins. Nature, 2011, 473(7346): 194.
[66]	VENTURELLI D, MANDRA S, KNYSH S, et al.Quantum optimization of fully connected spin glasses. Physical Review X, 2015, 5(3): 31040.
[67]	BUNYK P I, HOSKINSON E M, JOHNSON M W, et al.Architectural considerations in the design of a superconducting quantum annealing processor. IEEE Transactions on Applied Superconductivity, 2014, 24(4): 1-10.
[68]	WANG H, HE Y, LI Y H, et al.High-efficiency multiphoton boson sampling. Nature Photonics, 2017, 11(6): 361-365.
[69]	LIANG Q Y, VENKATRAMANI A V, CANTU S H, et al.Observation of three-photon bound states in a quantum nonlinear medium. Science, 2018, 359(6377): 783.
[70]	GOOGLE R.A Preview of Bristlecone, Google's New Quantum Processor.
[71]	BECKMAN D, CHARI A N, DEVABHAKTUNI S, et al.Efficient networks for quantum factoring. Physical Review A, 1996, 54(2): 1034-1063.
[72]	GROVER L K.A Fast Quantum Mechanical Algorithm for Database Search. STOC’96 Proceedings of the twenty-annaal ACM Symposium on Theory of Computing, 1996: 212-219.
[73]	GROVER L K.From Schrödinger's equation to the quantum search algorithm. Pramana, 2001, 56(2/3): 333-348.
[74]	GROVER L K.Quantum computing. Sciences, 1999, 39(4): 24-30.
[75]	AKL M N S G. Quantum Computation and Quantum Information. Cambridge University Press, 2000: 558-559.
[76]	SIMON D R.On the Power of Quantum Computation. Society for Industrial and Applied Mathematics, 1997: 1759-1768.
[77]	PASCAL KOIRAN V N, PORTIER N. A quantum lower bound for the query complexity of Simon's problem. Lecture Notes in Computer Science, 2005, 3580(1): 1287-1298.
[78]	JOZSA R.Quantum factoring, discrete logarithms, and the hidden subgroup problem. Computing in Science & Engineering, 2000, 3(2): 34-43.
[79]	SHOR P W.Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer. 1999: 303-332.
[80]	BUHLER J P, JR H W L, POMERANCE C. Factoring integers with the number field sieve. OAI, 1993, 5(3): 231-253.
[81]	LENSTRA A K, JR H W L. The Development of the Number Field Sieve. Springer-Verlag, 1993: 564-572.
[82]	MONTANARO A. Quantum algorithms: an overview. npj Quantum Information, 2016, 2: 15023-1-17.
[83]	GROVER L K.Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett., 1997, 79(2): 325-328.
[84]	BOYER M, BRASSARD G, H YER P, et al. Tight Bounds on Quantum Searching. Wiley‐VCH Verlag GmbH & Co. KGaA, 1998: 493-505.
[85]	AMBAINIS A, CHILDS A M, REICHARDT B W, et al.Any AND-OR Formula of Size N can be Evaluated in time N1/2 + o(1) on a Quantum Computer. 2007: 363-372.
[86]	SUN X, YAO A C, ZHANG S.Graph Properties and Circular Functions: How Low Can Quantum Query Complexity Go? 2004: 286-293.
[87]	BRASSARD G, HØYER P, MOSCA M, et al. Quantum amplitude amplification and estimation. Quantum Computation & Information. 2002, 5494: 53-74.
[88]	SCHÖNING U. A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems. 1999: 410.
[89]	HARROW A W, HASSIDIM A, LLOYD S.Quantum algorithm for linear systems of equations. Physical Review Letters, 2009, 103(15): 150502.
[90]	FARHI E, GOLDSTONE J, GUTMANN S, et al.Quantum Computation by Adiabatic Evolution. Quantum Physics, arxiv: quant-ph/0001106.
[91]	SUN X.A survey on quantum computing. Scientia Sinica Informationis, 2016, 46(8): 982.
[92]	WITTEK P.Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press, 2014.
[93]	NARAYANAN A, MOORE M.Quantum-inspired Genetic Algorithms. 1996: 61-66.
[94]	HAN K H, KIM J H.Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 2002, 6(6): 580-593.
[95]	YANG J, ZHUANG Z, SHI L.Multi-universe parallel quantum genetic algorithm. Acta Electronica Sinica, 2004, 32(6): 923-928.
[96]	CHEN H, ZHANG J, ZHANG C.Chaos Updating Rotated Gates Quantum-inspired Genetic Algorithm. 2004: 1108-1112.
[97]	WANG L, TANG F, WU H.Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Applied Mathematics and Computation, 2005, 171(2): 1141-1156.
[98]	WANG L.Advances in quantum-inspired evolutionary algorithms. Control and Decision, 2008, 23(12): 1321-1326.
[99]	PYLLKKÄNEN P, PYLLKKÖ P. New Directions in Cognitive Science. Creating Consilience: Integrating the Sciences & the Humanities. 1995.
[100]	KAK S.On Quantum Neural Computing. Elsevier Science Inc., 1995: 143-160.
[101]	KAK S C.The Three Languages Of The Brain: Quantum, Reorganizational, and Associative. 1996: 185-219.
[102]	GAUTAM A, KAK S.Symbols, meaning, and origins of mind. Biosemiotics, 2013, 6(3): 301-310.
[103]	DA SILVA A J E, LUDERMIR T B, DE OLIVEIRA W R. Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Networks, 2016, 76: 55-64.
[104]	PANELLA M, MARTINELLI G.Neural networks with quantum architecture and quantum learning. International Journal of Circuit Theory & Applications, 2011, 39(1): 61-77.
[105]	SCHULD M, SINAYSKIY I, PETRUCCIONE F.The quest for a Quantum Neural Network. Quantum Information Processing, 2014, 13(11): 2567-2586.
[106]	PATEL O, TIWARI A, PATEL V, et al.Quantum Based Neural Network Classifier and Its Application for Firewall to Detect Malicious Web Request. 2015: 67-74.
[107]	LI J.Quantum-inspired neural networks with application. Open Journal of Applied Sciences, 2015, 5(6): 233-239.
[108]	ALTAISKY M V, KAPUTKINA N E, KRYLOV V A.Quantum neural networks: current status and prospects for development. Physics of Particles & Nuclei. 2014, 45(6): 1013-1032.
[109]	FANG W, SUN J, XIE Z, et al.Convergence analysis of quantum- behaved particle swarm optimization algorithm and study on its control parameter. Acta Physica Sinica, 2009, 6(59): 3686-3694.
[110]	MANJU A, NIGAM M J.Applications of quantum inspired computational intelligence: a survey. Artificial Intelligence Review, 2014, 42(1): 79-156.
[111]	HOOFT G T.The cellular automaton interpretation of quantum mechanics. Physics Today, 2017, 70(7): 60.
[112]	LLOYD S.A theory of quantum gravity based on quantum computation. Quantum Physics, 2018.
[113]	YING M.Recent progress in the research of quantum programming. Communcations of the CCF, 2017, 13(1): 21-27.
[114]	PATNAIK S, YANG X, NAKAMATSU K.Nature-Inspired Computing and Optimization: Theory and Applications. Springer, 2017.
[115]	YANG X.Nature-inspired Computation in Engineering. Springer, 2016.
[116]	CHIONG R.Nature-inspired Algorithms for Optimisation. Springer, 2009.
[117]	DU K, SWAMY M.Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhäuser, 2016.
[118]	YANG X.Nature-inspired Metaheuristic Algorithms. Luniver Press, 2010.
[119]	BURKE E, KENDALL G, NEWALL J, et al.Hyper-heuristics: An Emerging Direction in Modern Search Technology. Handbook of Metaheuristics, Springer, 2003: 457-474.
[120]	DELORME A.Genetic Algorithm for Optimization of Mechanical Properties. Technical report, University of Cambridge, 2003.
[121]	HAN J, PEI J, KAMBER M.Data Mining: Concepts and Techniques. Elsevier, 2011.
[122]	FOSTER I, ZHAO Y, RAICU I, et al.Cloud Computing and Grid Computing 360-degree Compared. IEEE, 2008: 1-10.
[123]	ZHANG Q, CHENG L, BOUTABA R.Cloud computing: state- of-the-art and research challenges. Journal of Internet Services and Applications, 2010, 1(1): 7-18.
[124]	FISTER JR I, YANG X, FISTER I, et al.A brief review of nature- inspired algorithms for optimization. Elektrotehniški Vestnik, 2013, 80(3): 116-122.
[125]	YANG X.Recent Advances in Swarm Intelligence and Evolutionary Computation. Springer, 2015.
[126]	YANG X, KARAMANOGLU M.Swarm Intelligence and Bio-inspired Computation: An Overview. Swarm Intelligence and Bio-Inspired Computation, Elsevier, 2013: 3-23.
[127]	REISSNER H.Über die eigengravitation des elektrischen Feldes nach der Einsteinschen theorie. Annalen der Physik, 1916, 355(9): 106-120.
[128]	SCHWARZSCHILD K.Über das Gravitationsfeld einer Kugel aus inkompressibler Flüssigkeit nach der Einsteinschen theorie. 1916.
[129]	DROSTE J.On the field of a single centre in Einstein's theory of gravitation. Koninklijke Nederlandse Akademie van Wetenschappen Proceedings Series B Physical Sciences, 1915, 17: 998-1011.
[130]	HAWKING S W.Black hole explosions? Nature, 1974, 248(5443): 30-31.
[131]	DATTA S.Materials Design Using Computational Intelligence Techniques. Crc Press, 2015.
[132]	APOSTOLAKIS J.An introduction to data mining. Structure & Bonding, 2009, 134(472): 1-35.
[133]	PANGNING T, STEINBACH M, KUMAR V.Introduction to data mining. Data Analysis in the Cloud, 2014, 22(6): 1-25.
[134]	DATTA S, CHATTOPADHYAY P P.Soft computing techniques in advancement of structural metals. International Materials Reviews, 2013, 58(8): 475-504.
[135]	DATTA S, BANERJEE M K.Fuzzy modeling of strength- composition-process parameter relationships of HSLA steels. Materials and Manufacturing Processes, 2005, 20(5): 761-776.
[136]	DATTA S, MAHFOUF M, ZHANG Q, et al.Imprecise knowledge based design and development of titanium alloys for prosthetic applications. Journal of the Mechanical Behavior of Biomedical Materials, 2016, 53: 350-365.
[137]	DEY S, DEY P, DATTA S, et al.Rough set approach to predict the strength and ductility of TRIP steel. Materials and Manufacturing Processes, 2009, 24(2): 150-154.
[138]	SINGH J, GILL S S.Fuzzy modeling and simulation of ultrasonic drilling of porcelain ceramic with hollow stainless steel tools. Materials and Manufacturing Processes, 2009, 24(4): 468-475.
[139]	DEY S, DATTA S, CHATTOPADHYAY P P, et al.Modeling the properties of TRIP steel using AFIS: a distributed approach. Computational Materials Science, 2008, 43(3): 501-511.
[140]	DEHGHANNASIRI R, XUE D, BALACHANDRAN P V, et al.Optimal experimental design for materials discovery. Computational Materials Science, 2017, 129: 311-322.
[141]	GONG M, LI H, LUO E, et al.A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing. IEEE Transactions on Evolutionary Computation, 2017, 21(2): 234-248.
[142]	GONG M, WANG Z, ZHU Z, et al.A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system. IEEE Transactions on Evolutionary Computation, 2017, 21(6): 878-897.
[143]	YANG X S.Nature-Inspired Optimization Algorithms. Elsevier Science Publishers B. V., 2014: 1292.
[144]	WITTEN I H, FRANK E, HALL M A, et al.Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2016.