Journal of Inorganic Materials, Volume. 34, Issue 1, 27(2019)

Design of the Nature-inspired Algorithms Library and Its Significance for New Materials Research and Development

Shi-Yu DU1, Yi-Ming ZHANG1, Kan LUO1,2, Qing HUANG1, [in Chinese]1, [in Chinese]1, [in Chinese]1,2, and [in Chinese]1
Author Affiliations
  • 11. Engineering Laboratory of Nuclear Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
  • 22. School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
  • show less
    References(144)

    [1] , SUN X, XIANG X D. A combinatorial approach to materials discovery[D]. Science, 268, 1738(1995).

    [2] HUANG H Y, XIE J X, ZHU J. Recent progress and new ideas for accelerating research in rare earth steel[D]. Journal of Iron and Steel Research, 29, 513-529(2017).

    [3] LU W, RAMAKRISHNA S, ZHANG T et al[D]. Materials informatics. Journal of Intelligent Manufacturing, 2018, 1-20.

    [4] LI Y, LIU Z, SHI D et al. The development of cladding materials for the accident tolerant fuel system from the Materials Genome Initiative[D]. Scripta Materialia, 143, 129-136(2018).

    [5] . The development of material genome technology in the field of new energy materials[D]. Energy Storage Science and Technology, 6, 990(2017).

    [6] WHITE A A. Big data are shaping the future of materials science[D]. Mrs Bulletin, 38, 594-595(2013).

    [7] AGRAWAL A, CHOUDHARY A, WARD L et al. A general- purpose machine learning framework for predicting properties of inorganic materials[D]. npj Computational Materials, 2, 16028(2016).

    [8] GIBERTINI M, MOUNET N, SCHWALLER P et al. Two- dimensional materials from high-throughput computational exfoliation of experimentally known compounds[D]. Nature Nanotechnology, 13, 246-252(2018).

    [9] LI X, XU S, ZHAO Y et al. Two-dimensional semiconducting boron monolayers[D]. Journal of the American Chemical Society, 139, 17233-17236(2017).

    [10] JIN H M, SULLIVAN M B, TAN T L et al. High-throughput survey of ordering configurations in MXene alloys across compositions and temperatures[D]. ACS Nano, 11, 4407-4418(2017).

    [11] HUANG X, LIN J, ZHOU J et al. A library of atomically thin metal chalcogenides[D]. Nature, 556, 355(2018).

    [12] HANSON R J, KINCAID D R, LAWSON C L et al. Basic linear algebra subprograms for Fortran usage[D]. ACM Transactions on Mathematical Software (TOMS), 5, 308-323(1979).

    [13] ANDERSON E, BAI Z, BISCHOF C et al. LAPACK Users' Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA[D]. Society for Industrial and Applied Mathematics(1999).

    [14] CURTIN R, SANDERSON C. Armadillo: a template-based C++ library for linear algebra[D]. Journal of Open Source Software(2016).

    [15] [D]. Intel® Math Kernel Library Developer Reference(2017).

    [16] , DEMMEL J W, HEATH M T. Parallel numerical linear algebra[D]. Acta Numerica, 2, 111-197(1993).

    [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/[D]. CRC, 1435-1463(2004).

    [18] BAKSHEEV A, KORNYAKOV K, PULLI K et al. Real time computer vision with OpenCV[D]. Queue, 10, 40(2012).

    [19] CHAKRABORTI N. Genetic algorithms in materials design and processing[D]. International Materials Reviews, 49, 246-260(2004).

    [20] PASZKOWICZ W. Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields[D]. Materials and Manufacturing Processes, 24, 174-197(2009).

    [21] HKDH B. Neural networks in materials science[D]. ISIJ international, 39, 966-979(1999).

    [22] BHADESHIA H. Neural networks and information in materials science[D]. Statistical Analysis and Data Mining: The ASA Data Science Journal, 1, 296-305(2009).

    [23] BHADESHIA H, DIMITRIU R C, FORSIK S et al. Performance of neural networks in materials science. Materials Science and[D]. Technology., 25, 504-510(2009).

    [24] EVANS J, YANG S, ZHANG Y M. Revisiting Hume-Rothery's rules with artificial neural networks[D]. Acta Materialia, 56, 1094-1105(2008).

    [25] EVANS J, YANG S F, ZHANG Y M. Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations[D]. Philosophical Magazine, 90, 4453-4474(2010).

    [26] EVANS J R, YANG S, ZHANG Y. Corrected values for boiling points and enthalpies of vaporization of elements in handbooks[D]. Journal of Chemical and Engineering Data, 56, 328-337(2011).

    [27] UBIC R, XUE D F, ZHANG Y M et al. Predicting the structural stability and formability of ABO3-type perovskite compounds using artificial neural networks[D]. Materials Focus, 1, 57-64(2012).

    [28] CLOUTIER E, GUAY J, NADEAU R. New evidence about the existence of a bandwagon effect in the opinion formation process[D]. International Political Science Review, 14, 203-213(1993).

    [29] EARMAN J, MOSTERIN J. A critical look at inflationary cosmology[D]. Philosophy of Science, 66, 1-49(1999).

    [30] TRIMBLE V. Existence and nature of dark matter in the universe[D]. Annual Review of Astronomy & Astrophysics, 25, 425-472(1987).

    [31] EINSTEIN A, PODOLSKY B, ROSEN N. Can quantum- mechanical description of physical reality be considered complete?[D]. Phys. Rev., 47, 777-780(1935).

    [32] SHANNON C E. A mathematical theory of communication[D]. The Bell System Technical Journal, 27, 379-423(1948).

    [33] YANG X. A New Metaheuristic Bat-inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010),[D]. Springer, 65-74(2010).

    [34] KHAN K, SAHAI A. A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context[D]. International Journal of Intelligent Systems and Applications, 4, 23(2012).

    [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[D]. Engineering Structures, 159, 89-98(2018).

    [36] KHACHATURYAN A, SEMENOVSKAYA S, VAINSTEIN B. Statistical-thermodynamic approach to determination of structure amplitude phases. Sov. Phys[D]. Crystallography, 24, 519-524(1979).

    [37] KHACHATURYAN A, SEMENOVSOVSKAYA S, VAINSHTEIN B. The thermodynamic approach to the structure analysis of crystals. Acta Crystallographica Section A: Crystal Physics, Diffraction,[D]. Theoretical and General Crystallography, 37, 742-754(1981).

    [38] GELATT C D, KIRKPATRICK S, VECCHI M P. Optimization by simulated annealing[D]. Science, 220, 671-680(1983).

    [39] ANDERSON H L. METROPOLIS. Monte Carlo and the Maniac[D]. Los alamos Science, 14, 96-108(1986).

    [40] . Monte-Carlo Algorithms in Graph Isomorphism Testing[D]. Université tde Montréal Technical Report, DMS(1979).

    [41] LEVIN L A. The tale of one-way functions[D]. Problems of Information Transmission, 39, 92-103(2003).

    [42] GRUNDY D. Concepts and Calculation in Cryptography[D]. Citeseer(2008).

    [43] QUINLAN J R. Induction of decision trees[D]. Machine Learning, 1, 81-106(1986).

    [44] QUINLAN J R. C4.5: Programs for Machine Learning[D]. Elsevier(2014).

    [45] COULOM R E M. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search[D]. Springer, 72-83(2006).

    [46] KOCSIS L. SZEPESV A RI C. Bandit Based Monte-Carlo Planning[D]. Springer, 282-293(2006).

    [47] HUANG A, MADDISON C J, SILVER D et al. Mastering the game of Go with deep neural networks and tree search[D]. Nature, 529, 484-489(2016).

    [48] SCHRITTWIESER J, SILVER D, SIMONYAN K et al. Mastering the game of go without human knowledge[D]. Nature, 550, 354(2017).

    [49] FAN L, LIU Y H, ZHANG W. Ecological Pyramid Particle Swarm Optimization[D]. Computer Science, 44, 237-244(2017).

    [50] RAO R V, SAVSANI V J, VAKHARIA D P. Teaching- learning-based optimization: a novel method for constrained mechanical design optimization problems[D]. Computer-Aided Design, 43, 303-315(2011).

    [51] RAO R V, SAVSANI V J, VAKHARIA D P. Teaching- learning-based optimization: an optimization method for continuous non-linear large scale problems[D]. Information Sciences, 183, 1-15(2012).

    [52] DENG F, TUO S, YONG L. Survey of teaching-learning-based optimization algorithms[D]. Application Research of Computers, 30, 1933-1938(2013).

    [53] BI X, WANG J. Teaching-learning-based optimization algorithm with hybrid learning strategy[D]. Journal of Zhejiang University (Engineering Science), 51, 1024-1031(2017).

    [54] LIU K, TAN Y, ZHANG J et al. Random Black Hole Particle Swarm Optimization and Its Application[D]. IEEE, 359-365(2008).

    [55] HATAMLOU A. Black hole: a new heuristic optimization approach for data clustering[D]. Information Sciences, 222, 175-184(2013).

    [56] WARNANA D D. OTHERS. Black hole algorithm for determining model parameter in self-potential data[D]. Journal of Applied Geophysics, 148, 189-200(2018).

    [57] LIU Y, MA L, ZHU Y et al. A novel bionic algorithm inspired by plant root foraging behaviors[D]. Applied Soft Computing, 37, 95-113(2015).

    [58] DAN S. Biogeography-based optimization. IEEE Transactions on[D]. Evolutionary Computation., 12, 702-713(2008).

    [59] GOERTLER C, HUBERT W, WESCHE T. Modified habitat suitability index model for brown trout in Southeastern Wyoming[D]. North American Journal of Fisheries Management, 7, 232-237(1987).

    [60] DUAN X, WANG C, WANG N et al. Survey of Biogeography- based Optimization[D]. Computer Science, 37, 34-38(2010).

    [61] MA H, SIARRY P, SIMON D et al. Biogeography-based optimization: a 10-year review[D]. IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 391-407(2017).

    [62] BENIOFF P. The computer as a physical system: a microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines[D]. Journal of Statistical Physics, 22, 563-591(1980).

    [63] FEYNMAN R P. Simulating physics with computers[D]. International Journal of Theoretical Physics, 21, 467-488(1982).

    [64] DEUTSCH D. Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. R. Soc. Lond[D]. A, 400, 97-117(1985).

    [65] AMIN M H, GILDERT S, JOHNSON M W et al. Quantum annealing with manufactured spins[D]. Nature, 473, 194(2011).

    [66] KNYSH S, MANDRA S, VENTURELLI D et al. Quantum optimization of fully connected spin glasses[D]. Physical Review X, 5, 31040(2015).

    [67] BUNYK P I, HOSKINSON E M, JOHNSON M W et al. Architectural considerations in the design of a superconducting quantum annealing processor[D]. IEEE Transactions on Applied Superconductivity, 24, 1-10(2014).

    [68] HE Y, LI Y H, WANG H et al. High-efficiency multiphoton boson sampling[D]. Nature Photonics, 11, 361-365(2017).

    [69] CANTU S H, LIANG Q Y, VENKATRAMANI A V et al. Observation of three-photon bound states in a quantum nonlinear medium[D]. Science, 359, 783(2018).

    [70] GOOGLE R[D]. A Preview of Bristlecone, Google's New Quantum Processor..

    [71] BECKMAN D, CHARI A N, DEVABHAKTUNI S et al. Efficient networks for quantum factoring[D]. Physical Review A, 54, 1034-1063(1996).

    [72] GROVER L K. A Fast Quantum Mechanical Algorithm for Database Search. STOC’96 Proceedings of the twenty-annaal ACM Symposium on Theory of[C]. Computing, 212-219(1996).

    [73] GROVER L K. From Schrödinger's equation to the quantum search algorithm[D]. Pramana, 56, 333-348(2001).

    [74] GROVER L K. Quantum computing[D]. Sciences, 39, 24-30(1999).

    [75] [M]. AKL M N S G, 558-559(2000).

    [76] SIMON D R. On the Power of Quantum Computation[D]. Society for Industrial and Applied Mathematics, 1759-1768(1997).

    [77] PASCAL KOIRAN V N, PORTIER N. A quantum lower bound for the query complexity of Simon's problem[D]. Lecture Notes in Computer Science, 3580, 1287-1298(2005).

    [78] JOZSA R. Quantum factoring, discrete logarithms, and the hidden subgroup problem[D]. Computing in Science & Engineering, 3, 34-43(2000).

    [79] SHOR P W. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a[D]. Quantum Computer., 303-332(1999).

    [80] BUHLER J P. JR H W L, POMERANCE C. Factoring integers with the number field sieve[D]. OAI, 5, 231-253(1993).

    [81] , LENSTRA A K. The Development of the[D]. Number Field Sieve. Springer-Verlag, 564-572(1993).

    [82] 2: 15023-1-17[D]. MONTANARO A(2016).

    [83] GROVER L K. Quantum mechanics helps in searching for a needle in a haystack[D]. Phys. Rev. Lett., 79, 325-328(1997).

    [84] , BOYER M, BRASSARD G. Tight Bounds on Quantum Searching. Wiley‐VCH Verlag GmbH & Co[D]. KGaA, 493-505(1998).

    [85] AMBAINIS A, CHILDS A M, REICHARDT B W et al[D]. Any AND-OR Formula of Size N can be Evaluated in time N1/2 + o(1) on a Quantum Computer., 363-372(2007).

    [86] SUN X, YAO A C, ZHANG S[D]. Graph Properties and Circular Functions: How Low Can Quantum Query Complexity Go?, 286-293(2004).

    [87] BRASSARD G. HØYER P, MOSCA M, et al. Quantum amplitude amplification and estimation. Quantum Computation &[D]. Information., 5494, 53-74(2002).

    [88] SCHÖNING U. A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems[D], 410(1999).

    [89] HARROW A W, HASSIDIM A, LLOYD S. Quantum algorithm for linear systems of equations[D]. Physical Review Letters, 103, 150502(2009).

    [90] FARHI E, GOLDSTONE J, GUTMANN S et al. Quantum Computation by Adiabatic Evolution[D]. Quantum Physics, arxiv: quant-ph/0001106..

    [91] SUN X. A survey on quantum computing[D]. Scientia Sinica Informationis, 46, 982(2016).

    [92] WITTEK P.[M](2014).

    [93] MOORE M, NARAYANAN A. Quantum-inspired[D]. Genetic Algorithms., 61-66(1996).

    [94] HAN K H, KIM J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[D]. IEEE Transactions on Evolutionary Computation, 6, 580-593(2002).

    [95] SHI L, YANG J, ZHUANG Z. Multi-universe parallel quantum genetic algorithm[D]. Acta Electronica Sinica, 32, 923-928(2004).

    [96] CHEN H, ZHANG C, ZHANG J. Chaos Updating Rotated Gates Quantum-inspired[D]. Genetic Algorithm., 1108-1112(2004).

    [97] TANG F, WANG L, WU H. Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation[D]. Applied Mathematics and Computation, 171, 1141-1156(2005).

    [98] WANG L. Advances in quantum-inspired evolutionary algorithms[D]. Control and Decision, 23, 1321-1326(2008).

    [99] PYLLKKÄNEN P, PYLLKKÖ P. New Directions in Cognitive Science. Creating Consilience: Integrating the Sciences & the[D]. Humanities.(1995).

    [100] KAK S. On[D]. Quantum Neural Computing. Elsevier Science Inc., 143-160(1995).

    [101] KAK S C. The Three Languages Of The Brain: Quantum, Reorganizational, and[D]. Associative., 185-219(1996).

    [102] GAUTAM A, KAK S. Symbols, meaning, and origins of mind[D]. Biosemiotics, 6, 301-310(2013).

    [103] , DE OLIVEIRA W R, LUDERMIR T B. Quantum perceptron over a field and neural network architecture selection in a quantum computer[D]. Neural Networks, 76, 55-64(2016).

    [104] MARTINELLI G, PANELLA M. Neural networks with quantum architecture and quantum learning[D]. International Journal of Circuit Theory & Applications, 39, 61-77(2011).

    [105] PETRUCCIONE F, SCHULD M, SINAYSKIY I. The quest for a Quantum Neural Network[D]. Quantum Information Processing, 13, 2567-2586(2014).

    [106] PATEL O, PATEL V, TIWARI A et al. Quantum Based Neural Network Classifier and Its Application for Firewall to[D]. Detect Malicious Web Request., 67-74(2015).

    [107] LI J. Quantum-inspired neural networks with application[D]. Open Journal of Applied Sciences, 5, 233-239(2015).

    [108] ALTAISKY M V, KAPUTKINA N E, KRYLOV V A. Quantum neural networks: current status and prospects for development. Physics of Particles &[D]. Nuclei., 45, 1013-1032(2014).

    [109] FANG W, SUN J, XIE Z et al. Convergence analysis of quantum- behaved particle swarm optimization algorithm and study on its control parameter[D]. Acta Physica Sinica, 6, 3686-3694(2009).

    [110] MANJU A, NIGAM M J. Applications of quantum inspired computational intelligence: a survey[D]. Artificial Intelligence Review, 42, 79-156(2014).

    [111] HOOFT G T. The cellular automaton interpretation of quantum mechanics[D]. Physics Today, 70, 60(2017).

    [112] LLOYD S. A theory of quantum gravity based on quantum computation[D]. Quantum Physics(2018).

    [113] YING M. Recent progress in the research of quantum programming[D]. Communcations of the CCF, 13, 21-27(2017).

    [114] NAKAMATSU K, PATNAIK S, YANG X. Nature-Inspired Computing and Optimization: Theory and Applications[D]. Springer(2017).

    [115] YANG X. Nature-inspired Computation in Engineering[D]. Springer(2016).

    [116] CHIONG R. Nature-inspired Algorithms for Optimisation[D]. Springer(2009).

    [117] DU K, SWAMY M. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature[D]. Birkhäuser(2016).

    [118] YANG X.[M](2010).

    [119] BURKE E, KENDALL G, NEWALL J et al. Hyper-heuristics: An Emerging Direction in Modern Search Technology. Handbook of Metaheuristics,[D]. Springer, 457-474(2003).

    [120] DELORME A. Genetic Algorithm for Optimization of Mechanical Properties. Technical report,[D]. University of Cambridge(2003).

    [121] HAN J, KAMBER M, PEI J. Data Mining: Concepts and Techniques[D]. Elsevier(2011).

    [122] FOSTER I, RAICU I, ZHAO Y et al. Cloud Computing and Grid Computing 360-degree Compared[D]. IEEE, 1-10(2008).

    [123] BOUTABA R, CHENG L, ZHANG Q. Cloud computing: state- of-the-art and research challenges[D]. Journal of Internet Services and Applications, 1, 7-18(2010).

    [124] FISTER I, FISTER JR I, YANG X et al. A brief review of nature- inspired algorithms for optimization[D]. Elektrotehniški Vestnik, 80, 116-122(2013).

    [125] YANG X. Recent Advances in Swarm Intelligence and Evolutionary Computation[D]. Springer(2015).

    [126] KARAMANOGLU M, YANG X. Swarm Intelligence and Bio-inspired Computation: An Overview. Swarm Intelligence and Bio-Inspired Computation,[D]. Elsevier, 3-23(2013).

    [127] REISSNER H. Über die eigengravitation des elektrischen Feldes nach der Einsteinschen theorie[D]. Annalen der Physik, 355, 106-120(1916).

    [128] SCHWARZSCHILD K[D]. 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[C]. Koninklijke Nederlandse Akademie van Wetenschappen Proceedings Series B Physical Sciences, 17, 998-1011(1915).

    [130] HAWKING S W. Black hole explosions?[D]. Nature, 248, 30-31(1974).

    [131] DATTA S.[M](2015).

    [132] APOSTOLAKIS J. An introduction to data mining[D]. Structure & Bonding, 134, 1-35(2009).

    [133] KUMAR V, PANGNING T, STEINBACH M. Introduction to data mining[D]. Data Analysis in the Cloud, 22, 1-25(2014).

    [134] CHATTOPADHYAY P P, DATTA S. Soft computing techniques in advancement of structural metals[D]. International Materials Reviews, 58, 475-504(2013).

    [135] BANERJEE M K, DATTA S. Fuzzy modeling of strength- composition-process parameter relationships of HSLA steels[D]. Materials and Manufacturing Processes, 20, 761-776(2005).

    [136] DATTA S, MAHFOUF M, ZHANG Q et al. Imprecise knowledge based design and development of titanium alloys for prosthetic applications[D]. Journal of the Mechanical Behavior of Biomedical Materials, 53, 350-365(2016).

    [137] DATTA S, DEY P, DEY S et al. Rough set approach to predict the strength and ductility of TRIP steel[D]. Materials and Manufacturing Processes, 24, 150-154(2009).

    [138] GILL S S, SINGH J. Fuzzy modeling and simulation of ultrasonic drilling of porcelain ceramic with hollow stainless steel tools[D]. Materials and Manufacturing Processes, 24, 468-475(2009).

    [139] CHATTOPADHYAY P P, DATTA S, DEY S et al. Modeling the properties of TRIP steel using AFIS: a distributed approach[D]. Computational Materials Science, 43, 501-511(2008).

    [140] BALACHANDRAN P V, DEHGHANNASIRI R, XUE D et al. Optimal experimental design for materials discovery[D]. Computational Materials Science, 129, 311-322(2017).

    [141] GONG M, LI H, LUO E et al. A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing[D]. IEEE Transactions on Evolutionary Computation, 21, 234-248(2017).

    [142] GONG M, WANG Z, ZHU Z et al. A similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication system[D]. IEEE Transactions on Evolutionary Computation, 21, 878-897(2017).

    [143] YANG X S. Nature-Inspired Optimization Algorithms[D]. Elsevier Science Publishers B. V., 1292(2014).

    [144] FRANK E, HALL M A, WITTEN I H et al. Data Mining: Practical Machine Learning Tools and Techniques[D]. Morgan Kaufmann(2016).

    Tools

    Get Citation

    Copy Citation Text

    Shi-Yu DU, Yi-Ming ZHANG, Kan LUO, Qing HUANG, [in Chinese], [in Chinese], [in Chinese], [in Chinese]. 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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: May. 8, 2018

    Accepted: --

    Published Online: Feb. 4, 2021

    The Author Email:

    DOI:10.15541/jim20180214

    Topics