Bibliography

  1. [AGE 11] AGENCY I.E., “International energy Agency, Technology Roadmap Smart Grids”, IEA 2011, AIP Publishing, 2011.
  2. [ALB 02] ALBERT R., BARABÁSI A.-L., “Statistical mechanics of complex networks”, Rev. Mod. Phys., vol. 74, pp. 47–97, American Physical Society, January 2002.
  3. [ALB 04] ALBERT R., ALBERT I., NAKARADO G., “Structural vulnerability of the North American power grid”, Phys. Rev. E, vol. 69, p. 025103, American Physical Society, February 2004.
  4. [BAC 00] BÄCK T., FOGEL D., MICHALEWICZ Z., Evolutionary Computation 1: Basic Algorithms and Operators, vol. 1, CRC Press, 2000.
  5. [BAR 87] BARNARD S., “Stereo matching by hierarchical, microcanonical annealing”, Joint Conference on Artificial Intelligence, Milan, Italy, pp. 832–835, 1987.
  6. [BAT 94] BATTITI R., TECCHIOLLI G., “The Reactive Tabu Search”, INFORMS Journal on Computing, vol. 6, no. 2, pp. 126–140, 1994.
  7. [BEN 78] BENDER E., CANFIELD E., “The asymptotic number of labeled graphs with given degree sequences”, Journal of Combinatorial Theory, Series A, vol. 24, no. 3, pp. 296–307, Elsevier, May 1978.
  8. [BEN 05] BÉNICHOU O., COPPEY M., MOREAU M. et al., “Optimal search strategies for hidden targets”, Physical review letters, vol. 94, no. 19, p. 198101, APS, May 2005.
  9. [BER 73] BERGE C., Graphes et hypergraphes, Dunod, Paris, 1973.
  10. [BLE 90] BLELLOCH G., Prefix Sums and Their Applications, Report no. CMU-CS-90-190, School of Computer Science, Carnegie Mellon University, November 1990.
  11. [BLU 04] BLUM C., DORIGO M., “The hyper-cube framework for ant colony optimization”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 2, pp. 1161–1172, April 2004.
  12. [BOC 06] BOCCALETTI S., LATORA V., MORENO Y. et al., “Complex Networks: Structure and Dynamics”, Phys. Rep., vol. 424, pp. 175–308, Elsevier, February 2006.
  13. [BRA 85] BRANS J.-P., VINCKE P., “Note-A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making)”, Management science, vol. 31, no. 6, pp. 647–656, INFORMS, 1985.
  14. [BRA 03] BRAUNSTEIN L., BULDYREV S., COHEN R. et al., “Optimal paths in disordered complex networks”, Physical review letters, vol. 91, no. 16, p. 168701, APS, August 2003.
  15. [BRO 83] BROTHERS K., DUMOUCHEL W., PAULSON A., Fractiles of the stable laws, Report, Rensselaer Polytechnic Institute, Troy, NY, 1983.
  16. [CAR 99] CARLSON J., DOYLE J., “Highly optimized tolerance: A mechanism for power laws in designed systems”, Physical Review E, vol. 60, no. 2, pp. 1412–1427, APS, August 1999.
  17. [CAR 01] CARRERAS B., LYNCH V., SACHTJEN M. et al., “Modeling blackout dynamics in power transmission networks with simple structure”, HICSS, vol. 1, p. 2018, 2001.
  18. [CAR 02] CARRERAS B., LYNCH V., DOBSON I. et al., “Critical points and transitions in an electric power transmission model for cascading failure blackouts | Browse - Chaos”, Chaos, vol. 12, pp. 985–994, American Institute of Physics, December 2002.
  19. [CAR 09] CARVALHO R., BUZNA L., BONO F. et al., “Robustness of trans-European gas networks”, Phys. Rev. E, vol. 80, p. 016106, American Physical Society, July 2009.
  20. [CHA 76] CHAMBERS J., MALLOWS C., STUCK B., “A method for simulating stable random variables”, Journal of the American Statistical Association, vol. 71, no. 354, pp. 340–344, Taylor & Francis Group, June 1976.
  21. [CHA 93] CHARON I., HUDRY O., “The noising method: a new method for combinatorial optimization”, Operations Research Letters, vol. 14, no. 3, pp. 133–137, 1993.
  22. [CHA 06] CHARON I., HUDRY O., “Noising methods for a clique partitioning problem”, Discrete Applied Mathematics, vol. 154, no. 5, pp. 754–769, Elsevier, 2006.
  23. [CLE 02] CLERC M., KENNEDY J., “The particle swarm - explosion, stability, and convergence in a multidimensional complex space”, IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002.
  24. [CLE 06] CLERC M., Confinements and Biases in Particle Swarm Optimization, available at: http://clerc.maurice.free.fr/pso/Confinements_and_bias.pdf, 2006.
  25. [CLE 07] CLERC M., Binary Particle Swarm Optimisers: toolbox, derivations, and mathematical insights, available at: https://hal.archives-ouvertes.fr/hal-00122809/document, January 2007.
  26. [COH 00] COHEN R., EREZ K., BEN AVRAHAM D. et al., “Resilience of the Internet to Random Breakdowns”, Phys. Rev. Lett., vol. 85, pp. 4626–4628, American Physical Society, November 2000.
  27. [COH 01] COHEN R., EREZ K., AVRAHAM D. et al., “Breakdown of the Internet under Intentional Attack”, Physical Review Letters, vol. 86, pp. 3682–3685, American Physical Society, April 2001.
  28. [COH 02] COHEN R., BEN AVRAHAM D., HAVLIN S., “Percolation critical exponents in scale-free networks”, Phys. Rev. E, vol. 66, p. 036113, American Physical Society, September 2002.
  29. [COH 03] COHEN R., HAVLIN S., “Scale-Free Networks Are Ultrasmall”, Phys. Rev. Lett., vol. 90, p. 058701, American Physical Society, February 2003.
  30. [COR 06] CORREA E., FREITAS A., JOHNSON C., “A New Discrete Particle Swarm Algorithm Applied to Attribute Selection in a Bioinformatics Data Set”, Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO’06, New York, NY, USA, ACM, pp. 35–42, 2006.
  31. [COS 07] COSTA L., RODRIGUES F., TRAVIESO G. et al., “Characterization of Complex Networks: A Survey of Measurements”, Advances in Physics, vol. 56, no. 1, pp. 167–242, Taylor & Francis, February 2007.
  32. [CRE 83] CREUTZ M., “Microcanonical monte carlo simulation”, Physical Review Letters, vol. 50, no. 19, pp. 1411–1414, APS, 1983.
  33. [CRU 04] CRUCITTI P., LATORA V., MARCHIORI M., “Model for cascading failures in complex networks”, Phys. Rev. E, vol. 69, p. 045104, American Physical Society, April 2004.
  34. [CRU 05] CRUCITTI P., LATORA V., MARCHIORI M., “Locating critical lines in high-voltage electrical power grids”, Fluctuation and Noise Letters, vol. 5, no. 02, pp. L201–L208, World Scientific, 2005.
  35. [CUD 17] CUDA, NVIDIA OpenCL SDK Code Samples, https://developer.nvidia.com/opencl, 2017.
  36. [DAS 08] DAS S., ABRAHAM A., KONAR A., “Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives”, in LIU Y., SUN A., LOH H. et al. (eds.), Advances of Computational Intelligence in Industrial Systems, Springer, Berlin-Heidelberg, 2008.
  37. [DEN 90] DENEUBOURG J., ARON S., GOSS S. et al., “The self-organizing exploratory pattern of the argentine ant”, Journal of Insect Behavior, vol. 3, no. 2, pp. 159–168, 1990.
  38. [DEV 86] DEVROYE L., Non-uniform Random Variate Generation, Springer-Verlag, 1986.
  39. [DOB 01] DOBSON I., CARRERAS B., LYNCH V. et al., “An initial model fo complex dynamics in electric power system blackouts”, Proceedings of the 34th Annual Hawaii International Conference on System Sciences, pp. 710–718, January 2001.
  40. [DOR 91] DORIGO M., MANIEZZO V., COLORNI A., Positive feedback as a search strategy, Technical report 91–016, June 1991.
  41. [DOR 96] DORIGO M., MANIEZZO V., COLORNI A., “The ant systems: optimization by a colony of cooperative agents”, IEEE Transactions on Man, Machine and Cybernetics-Part B, vol. 26, no. 1, 1996.
  42. [DOR 97] DORIGO M., GAMBARDELLA L., “Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, 1997.
  43. [DOR 99a] DORIGO M., DI CARO G., “The Ant Colony Optimization Meta-heuristic”, in New Ideas in Optimization, McGraw-Hill, Maidenhead, 1999.
  44. [DOR 99b] DORIGO M., DI CARO G., GAMBARDELLA L., “Ant Algorithms for Discrete Optimization”, Artif. Life, vol. 5, no. 2, pp. 137–172, MIT Press, April 1999.
  45. [DOR 03] DOROGOVTSEV S., MENDES J., Evolution of Networks: From Biological Nets to the Internet and WWW (Physics), Oxford University Press, New York, 2003.
  46. [DOR 05] DORIGO M., BLUM C., “Ant colony optimization theory: A survey”, Theoretical Computer Science, vol. 344, nos. 2–3, pp. 243–278, 2005.
  47. [DOR 08] DOROGOVTSEV S., GOLTSEV A., MENDES J., “Critical phenomena in complex networks”, Rev. Mod. Phys., vol. 80, pp. 1275–1335, American Physical Society, October 2008.
  48. [DOY 00] DOYLE J., CARLSON J., “Power laws, highly optimized tolerance, and generalized source coding”, Physical Review Letters, vol. 84, no. 24, pp. 5656–9, APS, June 2000.
  49. [DRE 03] DREO J., PETROWSKI A., TAILLARD E. et al., Métaheuristiques pour l’optimisation difficile, Eyrolles, 2003.
  50. [DUV 71] DUVANENKO V., Parallel In-Place N-bit-Radix Sort, PhD Thesis, New Haven, 1971.
  51. [EBE 95] EBERHART R., KENNEDY J., “A new optimizer using particle swarm theory”, MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43, 1995.
  52. [EBE 96] EBERHART R., SIMPSON P., DOBBINS R., Computational Intelligence PC Tools, Academic Press Professional, 1996.
  53. [EBE 01] EBERHART R., SHI Y., KENNEDY J., Swarm Intelligence, 1st edition, Morgan Kaufmann, 2001.
  54. [EFT 04] EFTEKHARI A., “Fractal dimension of electrochemical reactions”, Journal of the Electrochemical Society, vol. 151, no. 9, pp. E291–E296, The Electrochemical Society, August 2004.
  55. [ELA 09] EL-ABD M., “Preventing premature convergence in a PSO and EDA hybrid”, IEEE Congress on Evolutionary Computation, CEC’09, pp. 3060–3066, May 2009.
  56. [ELD 12a] EL DOR A., CLERC M., SIARRY P., “Hybridization of Differential Evolution and Particle Swarm Optimization in a New Algorithm: DEPSO-2S”, Proceedings of the 2012 International Conference on Swarm and Evolutionary Computation, SIDE’12, Berlin, Heidelberg, Springer-Verlag, pp. 57–65, 2012.
  57. [ELD 12b] EL DOR A., CLERC M., SIARRY P., “A Multi-swarm PSO Using Charged Particles in a Partitioned Search Space for Continuous Optimization”, Comput. Optim. Appl., vol. 53, no. 1, pp. 271–295, Kluwer Academic Publishers, September 2012.
  58. [ERD 59] ERDÕS P., RÉNYI A., “On random graphs I.”, Publ. Math. Debrecen, vol. 6, pp. 290–297, 1959.
  59. [FAM 65] FAMA E., “The Behavior of Stock-Market Prices”, The Journal of Business, vol. 38, no. 1, pp. 34–105, The University of Chicago Press, January 1965.
  60. [FAM 68] FAMA E., ROLL R., “Some properties of symmetric stable distributions”, J. Am. Stat. Assoc., vol. 63, no. 323, pp. 817–836, American Statistical Association, September 1968.
  61. [FAM 71] FAMA E., ROLL R., “Parameter estimates for symmetric stable distributions”, Journal of the American Statistical Association, vol. 66, no. 334, pp. 331–338, Taylor & Francis, June 1971.
  62. [FEL 66] FELLER W., An Introduction to Probability Theory and its Applications, Vol. II., John Wiley and Sons, New York, 1966.
  63. [FEL 10] FELLER W., “An introduction to probability theory and its applications. Vol. II.”, Dr. Dobb’s Journal, August 2010.
  64. [FEN 09] FENIX PROJECT, http://www.fenix-project.org/, 2009.
  65. [FEO 95] FEO T., RESENDE M., “Greedy Randomized Adaptive Search Procedures”, Journal of Global Optimization, vol. 6, no. 2, pp. 109–133, 1995.
  66. [FER 03] FERRER I CANCHO, SOLÉ R., “Optimization in complex networks”, Statistical mechanics of complex networks, pp. 114–126, Springer, 2003.
  67. [FEU 81] FEUERVERGER A., MCDONNOUGH P., “On efficient inference in symmetric stable laws and processes”, Canadian Journal of Statistics, vol. 18, no. 2, pp. 109–122, Statistics and Related topics, 1981.
  68. [FIE 72] FIELITZ B., SMITH E., “Asymmetric stable distributions of stock price changes”, Journal of the American Statistical Association, vol. 67, no. 340, pp. 813–814, Taylor & Francis, January 1972.
  69. [FIG 05] FIGUEIRA J., MOUSSEAU V., ROY B., “ELECTRE methods”, Multiple criteria decision analysis: State of the art surveys, pp. 133–153, Springer, 2005.
  70. [FRE 81] FREUND R., LITTELL R., SAS for linear models: a guide to the ANOVA and GLM procedures, vol. 1, Sas Institute, 1981.
  71. [FRE 11] FREEMAN M., Digital Slr Handbook, Ilex, 3rd edition, 2011.
  72. [GAL 05] GALLOS L., COHEN R., ARGYRAKIS P. et al., “Stability and topology of scale-free networks under attack and defense strategies”, Physical review letters, vol. 94, no. 18, p. 188701, APS, May 2005.
  73. [GEH 65a] GEHAN E., “A generalized two-sample Wilcoxon test for doubly censored data”, Biometrika, pp. 650–653, JSTOR, 1965.
  74. [GEH 65b] GEHAN E., “A generalized Wilcoxon test for comparing arbitrarily singly-censored samples”, Biometrika, vol. 52, nos. 1–2, pp. 203–223, Biometrika Trust, 1965.
  75. [GEN 16] GENERAL ELECTRIC GRID SOLUTIONS, Internal resources, 2016.
  76. [GEO 74] GEOFFRION A., “Lagrangean relaxation for integer programming”, dans BALINSKI M. (eds.), Approaches to Integer Programming, vol. 2, Mathematical Programming Studies, pp. 82–114, Springer, Berlin-Heidelberg, 1974.
  77. [GLO 86a] GLOVER F., “Future paths for integer programming and links to artificial intelligence”, Computers & Operations Research, vol. 13, no. 5, pp. 533–549, 1986.
  78. [GLO 86b] GLOVER F., MCMILLAN C., “The general employee scheduling problem. An integration of MS and AI”, Computers & Operations Research, vol. 13, no. 5, pp. 563–573, Elsevier, 1986.
  79. [GOL 89] GOLDBERG D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., 1st edition, 1989.
  80. [GOL 91] GOLDBERG D., DEB K., “A comparative analysis of selection schemes used in genetic algorithms”, Foundations of Genetic Algorithms, vol. 1, pp. 69–93, 1991.
  81. [GOT 82] GOTOH O., “An improved algorithm for matching biological sequences”, Journal of Molecular Biology, vol. 162, no. 3, pp. 705–708, 1982.
  82. [GRO] GROUP T.K., “OpenCL API 1.1 Quick Reference Card”, http://www.khronos.org.
  83. [HAN 95] HANSEN N., OSTERMEIER A., GAWELCZYK A., “On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation”, Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 57–64, 1995.
  84. [HAN 00] HANSEN P., JAUMARD B., MLADENOVIĆ N. et al., “Variable neighborhood search for weight satisfiability problem”, Les Cahiers du GERARD, G-2000-62, 2000.
  85. [HAN 01a] HANSEN P., MLADENOVI N., “Variable neighborhood search: Principles and applications”, European Journal Of Operational Research, vol. 130, no. 3, pp. 449–467, 2001.
  86. [HAN 01b] HANSEN P., MLADENOVIĆ N., PEREZ-BRITOS D., “Variable neighborhood decomposition search”, Journal of Heuristics, vol. 7, no. 4, pp. 335–350, 2001.
  87. [HAN 09] HANSEN N., FINCK S., ROS R. et al., Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions, Research Report no. RR–6829, INRIA, 2009.
  88. [HAU 04] HAUPT R., HAUPT S., Practical Genetic Algorithms, John Wiley & Sons, 2004.
  89. [HEP 90] HEPPNER F, GRENANDER U., “A stochastic nonlinear model for coordinated bird flocks”, American Association for the Advancement of Science, 1990.
  90. [HER 93] HERAULT L., HORAUD R., “Figure-ground discrimination: A combinatorial optimization approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 899–914, 1993.
  91. [HIL 86] HILLIS W., STEELE JR. G., “Data Parallel Algorithms”, Commun. ACM, vol. 29, no. 12, pp. 1170–1183, ACM, December 1986.
  92. [HIL 90] HILLIS W., “Simulated Evolution”, Optimization, vol. 42, pp. 228–234, 1990.
  93. [HOL 73] HOLD D., CROW E., “Tables and graphs of the stable probability functions”, Journal of Rechearch of the National Bureau of Standards, vols. 3–4, no. 77b, pp. 143–198, B. Mathematical Sciences, July 1973.
  94. [HOL 92] HOLLAND J., Adaptation in Natural and Artificial Systems, 1992.
  95. [HSI 09] HSIEH S.-T., SUN T.-Y., LIU C.-C. et al., “Efficient population utilization strategy for particle swarm optimizer”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 2, pp. 444–456, April 2009.
  96. [HSU 08] HSU H., LACHENBRUCH P., “Paired t test”, Wiley Encyclopedia of Clinical Trials, Wiley Online Library, 2008.
  97. [IEA 11] IEA, Technology Roadmap Smart Grids, International Energy Agency, 2011.
  98. [JAM 94] JAMES F., “RANLUX: A Fortran implementation of the high-quality pseudorandom number generator of Lüscher”, Computer Physics Communications, vol. 79, no. 1, pp. 111–114, Elsevier, September 1994.
  99. [JUN 94] JUN G., HUANG X., “Efficient local search with search space smoothing: a case study of the traveling salesman problem (TSP)”, IEEE Transactions on Systems, Man and Cybernetics, vol. 24, no. 5, pp. 728–735, May 1994.
  100. [KAN 75] KANTER M., “Stable densities under change of scale and total variation inequalities”, The Annals of Probability, vol. 3, no. 4, pp. 697–707, Institute of Mathematical Statistics, August 1975.
  101. [KAR 72] KARP R., “Reducibility among combinatorial problems”, in Complexity of Computer Computations, Yorktown Heights, 1972.
  102. [KAR 98] KARYPIS G., KUMAR V., METIS: A Software Package for Partitioning Unstructured Graphs, Partitioning Meshes, and Computing Fill-Reducing Orderings of Sparse Matrices, September 1998.
  103. [KEN 95] KENNEDY J., EBERHART R., “Particle swarm optimization”, IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948, 1995.
  104. [KEN 02] KENNEDY J., MENDES R., “Population structure and particle swarm performance”, Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1671–1676, 2002.
  105. [KIM 00] KIM H.-J., KIM H.-J., FAY M.P., FEUER E.J., MIDTHUNE D.N., “Permutation tests for joinpoint regression with applications to cancer rates”, Statistics in Medicine, vol. 19, pp. 335–351, 2000.
  106. [KIN 05] KINNEY R., CRUCITTI P., ALBERT R. et al., “Modeling cascading failures in the North American power grid”, The European Physical Journal B - Condensed Matter and Complex Systems, vol. 46, no. 1, pp. 101–107, EDP Sciences, July 2005.
  107. [KIR 83] KIRKPATRICK S., GELATT C. D. JR., VECCHI M. P., “Optimization by Simulated Annealing”, Science, vol. 220, pp. 671–680, 1983.
  108. [KIR 84] KIRKPATRICK S., “Optimization by simulated annealing: Quantitative studies”, Journal of Statistical Physics, vol. 34, pp. 975–986, 1984.
  109. [KOG 98] KOGON S., WILLIAMS D., A Practical Guide to Heavy Tails, Birkhauser Boston Inc., 1998.
  110. [KOU 80] KOUTROUVELIS I., “Regression-type estimation of the parameters of stable laws”, Journal of the American Statistical Association, vol. 75, no. 372, pp. 918–928, Taylor & Francis, December 1980.
  111. [LEI 75] LEITCH R., PAULSON A., “Estimation of stable law parameters: stock price behavior application”, Journal of the American Statistical Association, vol. 70, no. 351a, pp. 690–697, Taylor & Francis, September 1975.
  112. [LEV 02] LÉVY VÉHEL J., WALTER C., Les Marchés fractals, PUF, Paris, 2002.
  113. [LIA 13] LIANG J., QU B., SUGANTHAN P. et al., Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 2013.
  114. [LIN 96] LINDGREN F., HANSEN B., KARCHER W. et al., “Model validation by permutation tests: Applications to variable selection”, Journal of Chemometrics, vol. 10, nos. 5–6, pp. 521–532, Wiley Online Library, 1996.
  115. [LIU 05] LIU J., LAMPINEN J., “A Fuzzy Adaptive Differential Evolution Algorithm”, Soft Computing, vol. 9, no. 6, pp. 448–462, Springer-Verlag, 2005.
  116. [LOU 01] LOURENCÓ H., MARTIN O., STÜTZLE T., “A beginner’s introduction to Iterated Local Search”, Proceeding of the 4th Metaheuristics International Conference, pp. 1–11, 2001.
  117. [LOU 03] LOURENĆO H., MARTIN O., STÜTZLE T., “Iterated Local Search”, in GLOVER F., KOCHENBERGER G. (eds.), Handbook of Metaheuristics, Springer US, 2003.
  118. [LUK 09] ŁUKASIK S., ŻAK S., “Firefly Algorithm for Continuous Constrained Optimization Tasks”, in NGUYEN N., KOWALCZYK R., CHEN S.-M. (eds.), Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, Springer, Berlin-Heidelberg, 2009.
  119. [LUS 94] LÜSCHER M., “A portable high-quality random number generator for lattice field theory simulations”, Computer Physics Communications, vol. 79, no. 1, pp. 100–110, Elsevier, February 1994.
  120. [MA 10] MA H., “An analysis of the equilibrium of migration models for biogeography-based optimization”, Information Sciences, vol. 180, no. 18, pp. 3444–3464, Elsevier, 2010.
  121. [MAC 67] MACARTHUR R., WILSON E., The Theory of Island Biogeography, vol. 1, Princeton University Press, 1967.
  122. [MAN 63] MANDELBROT B., “The Variation of Certain Speculative Prices”, The Journal of Business, vol. 36, pp. 394–419, The University of Chicago Press, 1963.
  123. [MAN 94] MANTEGNA R., “Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes”, Phys. Rev. E, vol. 49, pp. 4677–4683, American Physical Society, May 1994.
  124. [MAN 05] MANNING M., CARLSON J., DOYLE J., “Highly optimized tolerance and power laws in dense and sparse resource regimes”, Physical Review E, vol. 72, no. 1, p. 016108, APS, July 2005.
  125. [MAR 04] MARSHALL L.F., “The Lagrangian Relaxation Method for Solving Integer Programming Problems”, Management Science, vol. 50, no. 12, supplément, pp. 1861–1871, 2004.
  126. [MAS 51] MASSEY JR F., “The Kolmogorov-Smirnov test for goodness of fit”, Journal of the American Statistical Association, vol. 46, no. 253, pp. 68–78, Taylor & Francis Group, March 1951.
  127. [MCC 86] MCCULLOCH J., “Simple consistent estimators of stable distribution parameters”, Communications in Statistics-Simulation and Computation, vol. 15, no. 4, pp. 1109–1136, Taylor & Francis, 1986.
  128. [MET 53] METROPOLIS N., ROSENBLUTH A., ROSENBLUTH M. et al., “Equation of state calculations by fast computing machines”, The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, AIP Publishing, 1953.
  129. [MIL 56] MILLER L., “Table of percentage points of Kolmogorov statistics”, Journal of the American Statistical Association, vol. 51, no. 273, pp. 111–121, Taylor & Francis, March 1956.
  130. [MIT 01] MITTNIK S., Stable Non-Gaussian Models in Finance and Econometrics, vol. 34, Pergamon Press, NewYork, 2001.
  131. [MLA 97] MLADENOVIĆ N., HANSEN P., “Variable neighborhood search”, Computers & Operations Research, vol. 24, no. 11, pp. 1097–1100, 1997.
  132. [MON 84] MONTGOMERY D., Design and Analysis of Experiments, Wiley, 1984.
  133. [MOT 02] MOTTER A., LAI Y.-C., “Cascade-based attacks on complex networks”, Phys. Rev. E, vol. 66, p. 065102, American Physical Society, December 2002.
  134. [NAK 13] NAKIB A., SIARRY P., “Performance Analysis of Dynamic Optimization Algorithms”, in ALBA E., NAKIB A., SIARRY P. (eds.), Metaheuristics for Dynamic Optimization, Springer, Berlin-Heidelberg, 2013.
  135. [NAK 15] NAKIB A., THIBAULT B., SIARRY P., “Bayesian based metaheuristic for large scale continuous optimization”, IEEE/ACM International Parallel and Distributed Processing, Hyderabad, India, May 2015.
  136. [NEL 65] NELDER J., MEAD R., “A Simplex Method for Function Minimization”, The Computer Journal, vol. 7, no. 4, pp. 308–313, 1965.
  137. [NEW 02] NEWMAN M., “Assortative Mixing in Networks”, Phys. Rev. Lett., vol. 89, p. 208701, American Physical Society, October 2002.
  138. [NEW 03] NEWMAN M., “The Structure and Function of Complex Networks”, SIAM Review, vol. 45, pp. 167–256, SIAM, August 2003.
  139. [NIK 95] NIKIAS C., SHAO M., Signal Processing with Alpha-stable Distributions and Applications, Wiley-Interscience, New York, 1995.
  140. [NOL 98] NOLFI S., FLOREANO D., “Coevolving predator and prey robots: do “arms races” arise in artificial evolution”, Artificial life, vol. 4, no. 4, pp. 311–335, 1998.
  141. [NOT 11] NOTTALE L., Scale Relativity and Fractal Space-Time: A New Approach to Unifying Relativity and Quantum Mechanics, Imperial College Press, 2011.
  142. [PAN 92] PANTON D., “Cumulative distribution function values for symmetric standardized stable distributions”, Communications in Statistics-Simulation and Computation, vol. 21, no. 2, pp. 485–492, Taylor & Francis, 1992.
  143. [PAS 01] PASTOR-SATORRAS R., VÁZQUEZ A., VESPIGNANI A., “Dynamical and Correlation Properties of the Internet”, Phys. Rev. Lett., vol. 87, p. 258701, American Physical Society, November 2001.
  144. [PAU 75] PAULSON A., HOLCOMB E., LEITCH R., “The estimation of the parameters of the stable laws”, Biometrika, vol. 62, no. 1, pp. 163–170, Biometrika Trust, April 1975.
  145. [PAU 07] PAUL G., COHEN R., SREENIVASAN S. et al., “Graph Partitioning Induced Phase Transitions”, Phys. Rev. Lett., vol. 99, p. 115701, American Physical Society, September 2007.
  146. [POO 95] POON P., CARTER J., “Genetic algorithm crossover operators for ordering applications”, Computers & Operations Research, vol. 22, no. 1, pp. 135–147, 1995.
  147. [POT 94] POTTER M., DE JONG K., “A cooperative coevolutionary approach to function optimization”, Parallel Problem Solving from Nature, pp. 249–257, 1994.
  148. [PRE 72] PRESS S., “Estimation in univariate and multivariate stable distributions”, Journal of the American Statistical Association, vol. 67, no. 340, pp. 842–846, Taylor & Francis Group, December 1972.
  149. [PRE 92] PRESS W., Numerical Recipes in C: The Art of Scientific Computing, no. 4, William H. Press, Cambridge University Press, 1992.
  150. [QIN 05] QIN A., SUGANTHAN P., “Self-adaptive differential evolution algorithm for numerical optimization”, 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791, September 2005.
  151. [QIN 09a] QIN A., HUANG V., SUGANTHAN P., “Differential evolution algorithm with strategy adaptation for global numerical optimization”, IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, April 2009.
  152. [QIN 09b] QING A., Benchmarking a Single-Objective Optimization Test Bed for Parametric Study on Differential Evolution, John Wiley & Sons, 2009.
  153. [RAD 91] RADCLIFFE N., “Equivalence class analysis of genetic algorithms”, Complex Systems, vol. 5, no. 2, pp. 183–205, 1991.
  154. [REC 73] RECHENBERG I., “Evolution Strategy: optimization of technical systems by means of biological evolution”, Fromman-Holzboog, vol. 104, 1973.
  155. [REY 87] REYNOLDS C., “Flocks, herds and schools: a distributed behavioral model”, SIGGRAPH Comput. Graph., vol. 21, no. 4, pp. 25–34, ACM, July 1987.
  156. [REY 94] REYNOLDS R., ZANNONI E., POSNER R., “Learning to understand software using cultural algorithms”, Conference On Evolutionary Programming, San Diego, CA, pp. 150–157, 1994.
  157. [REY 05] REYNOLDS R., PENG B., “Cultural Algorithms: Computational Modeling of How Cultures Learn To Solve Problems: an Engineering Example”, Cybernetics & Systems, vol. 36, no. 8, pp. 753–771, 2005.
  158. [RON 97] RONALD S., “Robust encodings in genetic algorithms: a survey of encoding issues”, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC ’97), 1997.
  159. [ROS 97] ROSIN C., BELEW R., “New methods for competitive coevolution”, Evolutionary Computation, vol. 5, no. 1, pp. 1–29, 1997.
  160. [ROS 12] ROSAS CASALS M., COROMINAS MURTRA B. et al., Assessing European power grid reliability by means of topological measures, Report, 2012.
  161. [ROW 04] ROWE J., WHITLEY D., BARBULESCU L. et al., “Properties of gray and binary representations.”, Evolutionary Computation, vol. 12, no. 1, pp. 47–76, 2004.
  162. [ROY 68] ROY B., “Classement et choix en présence de points de vue multiples”, Revue française d’automatique, d’informatique et de recherche opérationnelle. Recherche opérationnelle, vol. 2, no. 1, pp. 57–75, 1968.
  163. [ROZ 09] ROZEL B., La sécurisation des infrastructures critiques: recherche d’une méthodologie d’identification des vulnérabilités et modélisation des interdépendances, PhD Thesis, Paris, France, 2009.
  164. [RUX 06] RUXTON G., “The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test”, Behavioral Ecology, vol. 17, no. 4, pp. 688–690, ISBE, 2006.
  165. [SAM 69] SAMUELSON P., “Lifetime portfolio selection by dynamic stochastic programming”, The Review of Economics and Statistics, vol. 51, no. 3, pp. 239–246, 1969.
  166. [SCH 81] SCHWEFEL H.-P., Numerical Optimization of Computer Models, John Wiley & Sons, 1981.
  167. [SCH 02] SCHWARTZ N., COHEN R., BEN-AVRAHAM D. et al., “Percolation in directed scale-free networks”, Physical Review E, vol. 66, no. 1, p. 015104, APS, August 2002.
  168. [SHA 03] SHARGEL B., SAYAMA H., EPSTEIN I. et al., “Optimization of Robustness and Connectivity in Complex Networks”, Phys. Rev. Lett., vol. 90, p. 068701, American Physical Society, February 2003.
  169. [SHI 98] SHI Y., EBERHART R., “A modified particle swarm optimizer”, IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73, 1998.
  170. [SHI 99] SHI Y., EBERHART R., “Empirical study of particle swarm optimization”, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, pp. 1945–1950, 1999.
  171. [SIM 08] SIMON D., “Biogeography-Based Optimization”, IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, December 2008.
  172. [SOI 96] SOILLE P., RIVEST J., “On the Validity of Fractal Dimension Measurements in Image Analysis”, Journal of Visual Communication and Image Representation, vol. 7, no. 3, pp. 217–229, Elsevier, September 1996.
  173. [SOL 08] SOLÉ R., ROSAS-CASALS M., COROMINAS-MURTRA B. et al., “Robustness of the European power grids under intentional attack”, Phys. Rev. E, vol. 77, p. 026102, American Physical Society, February 2008.
  174. [SOL 16] SOLUTIONS G.E.G., Internal report on Smart Grids, Report, 2016.
  175. [SON 07] SONG C., GALLOS L.K., HAVLIN S. et al., “How to calculate the fractal dimension of a complex network: the box covering algorithm”, Journal of Statistical Mechanics: Theory and Experiment, vol. 2007, no. 03, p. P03006, IOPscience, January 2007.
  176. [SPE 62] SPENDLEY W., HEXT G., HIMSWORTH F., “Sequential application of simplex designs in optimisation and evolutionary operation”, Technometrics, vol. 4, no. 4, pp. 441–461, Taylor & Francis Group, 1962.
  177. [STE 70] STEPHENS M., “Use of the Kolmogorov-Smirnov, Cramér-Von Mises and related statistics without extensive tables”, Journal of the Royal Statistical Society. Series B (Methodological), vol. 32, no. 1, pp. 115–122, JSTOR, 1970.
  178. [STE 03] STÉPHANE G., Métaheuristiques appliquées au placement optimal de dispositifs FACTS dans un réseau électrique, PhD Thesis, Lausanne, Suisse, 2003.
  179. [STO 95] STORN R., PRICE K., Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, vol. 3, ICSI Berkeley, 1995.
  180. [STU 00] STÜTZLE T., HOOS H., “Ant System”, Future Generation Computer Systems, vol. 16, no. 8, pp. 889–914, 2000.
  181. [TAI 91] TAILLARD E., “Robust tabu search for the quadratic assignment problem”, Parallel computing, vol. 17, pp. 443–455, 1991.
  182. [TAL 09] TALBI E.-G., Metaheuristics: From Design to Implementation, John Wiley & Sons, 2009.
  183. [TAN 05] TANIZAWA T., PAUL G., COHEN R. et al., “Optimization of network robustness to waves of targeted and random attacks”, Phys. Rev. E, vol. 71, p. 047101, American Physical Society, April 2005.
  184. [TAN 10] TAN Y., ZHU Y., “Fireworks algorithm for optimization”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6145 LNCS, no. PART 1, pp. 355–364, 2010.
  185. [TAN 15] TAN Y., Fireworks Algorithm: A Novel Swarm Intelligence Optimization Method, Springer, 2015.
  186. [TEA 06] TEAM B., Areva, Internal Report, 2006.
  187. [THE 90] THEILER J., “Estimating fractal dimension”, JOSA A, vol. 7, no. 6, pp. 1055–1073, Optical Society of America, June 1990.
  188. [TOL 03] TOLLE C., MCJUNKIN T., GORISCH D., “Suboptimal minimum cluster volume cover-based method for measuring fractal dimension”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 32–41, January 2003.
  189. [VOU 99] VOUDOURIS C., TSANG E., “Guided local search and its application to the traveling salesman problem”, European Journal of Operational Research, vol. 113, no. 2, pp. 469–499, 1999.
  190. [WAN 06] WANG B., TANG H., GUO C. et al., “Entropy optimization of scalefree networksŕobustness to random failures”, Physica A: Statistical Mechanics and its Applications, vol. 363, no. 2, pp. 591–596, Elsevier, May 2006.
  191. [WAN 08a] WANG B., KAZUYUKI A., LUONAN C., “Traffic jamming in disordred flow distribution networks”, Operations Research and Its Applications: The 7th International Symposium, ISORA ’08, Lecture notes in operations research, Lijiang, Chine, World Publishing Corporation, pp. 465–469, 2008.
  192. [WAN 08b] WANG Y., XIAO S., XIAO G. et al., “Robustness of Complex Communication Networks Under Link Attacks”, Proceedings of the 2008 International Conference on Advanced Infocomm Technology (ICAIT ’08) , New York, pp. 61:1–61:7, 2008.
  193. [WAS 94] WASSERMAN S., FAUST K., Social Network Analysis: Methods and Applications, vol. 8, Structural Analysis in the Social Sciences, Cambridge University Press, Cambridge, 1994.
  194. [WAT 98] WATTS D., STROGATZ S., “Collective dynamics of ’small-world’networks”, Nature, vol. 393, no. 6684, pp. 440–442, Nature Publishing Group, April 1998.
  195. [WAT 02] WATTS D., “A simple model of global cascades on random networks”, Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 9, p. 5766, National Academy of Sciences, May 2002.
  196. [WER 95] WERON R., Performance of the estimators of stable law parameters, HSC Research Reports no. HSC/95/01, Hugo Steinhaus Center, Wroclaw University of Technology, 1995.
  197. [WHI 01] WHITLEY E., BALL J., “Statistics review 1: Presenting and summarising data”, Critical Care, vol. 6, no. 1, p. 66, BioMed Central Ltd, 2001.
  198. [WOR 75] WORSDALE G., “Tables of cumulative distribution functions for symmetric stable distributions”, Applied Statistics, vol. 24, no. 1, pp. 123–131, JSTOR, November 1975.
  199. [WU 06] WU Z., BRAUNSTEIN L., COLIZZA V. et al., “Optimal paths in complex networks with correlated weights: The worldwide airport network”, Phys. Rev. E, vol. 74, p. 056104, American Physical Society, November 2006.
  200. [WU 08] WU Z., PENG G., WANG W.-X. et al., “Cascading failure spreading on weighted heterogeneous networks”, Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, p. P05013, IOPscience, March 2008.
  201. [XU 14] XU J., ZHANG J., “Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis”, 33rd Chinese Control Conference (CCC), Nanjing, China, pp. 8633–8638, July 2014.
  202. [YAN 08] YANG X.-S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008.
  203. [YAN 09a] YANG X.-S., Firefly Algorithms for Multimodal Optimization, Springer, Berlin, 2009.
  204. [YAN 09b] YANG X.-S., DEB S., “Cuckoo Search via Lévy flights”, Nature Biologically Inspired Computing, NaBIC 2009, Coimbatore, India, pp. 210–214, December 2009.
  205. [YAN 10] YANG X.-S., “Firefly algorithm, stochastic test functions and design optimisation”, International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78–84, 2010.
  206. [YOS 00] YOSHIDA H., KAWATA K., FUKUYAMA Y. et al., “A particle swarm optimization for reactive power and voltage control considering voltage security assessment”, IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1232–1239, 2000.
  207. [ZEM 81] ZEMEL E., “Measuring the Quality of Approximate Solutions to Zero-One Programming Problems”, Mathematics of Operations Research, vol. 6, no. 3, pp. 319–332, 1981.
  208. [ZHA 10] ZHANG Z., GAO S., CHEN L. et al., “Mapping Koch curves into scale-free small-world networks”, Journal of Physics A: Mathematical and Theoretical, vol. 43, no. 39, p. 395101, IOP Publishing, 2010.
  209. [ZLO 02] ZLOCHIN M., DORIGO M., “Model-based search for combinatorial optimization: A comparative study”, Parallel Problem Solving from Nature-PPSN VII, pp. 651–661, Springer, 2002.
  210. [ZOL 66] ZOLOTAREV V., “On representation of stable laws by integrals”, Selected Translations in Mathematical Statistics and Probability, vol. 6, no. 1, pp. 84–88, American Mathematical Society Providence, 1966.
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