Métaheuristiques pour l'optimisation combinatoire et l'affectation sous contraintes

Revue d'Intelligence Artificielle

Volumn 13, Issue 2, 1999, Pages 283-324

  Hao, Jin Kao ^a   Galinier, Philippe ^b   Habib, Michel ^c  

^a UNIVERSITÉ D'ANGERS   (France)

^b ECOLE DES MINES D ALÈS   (France)

^c LIRMM   (France)

Author keywords

Combinatorial optimisation; Constrained problems; Evolutionary algorithms; Heuristics; Hybrid methods; Metaheuristics; Neighbourhood search

Indexed keywords

EID: 0346933151     PISSN: 0992499X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (185)

1. E.H.L. AARTS, J. KORST, Simulated annealing and boltzmann machines: a stochastic approach to combinatorial and neural computing. Wiley, Chichester, 1989.
2. E.H.L. AARTS, J.K. LENSTRA (Eds.), Local search in combinatorial optimization, John Wiley & Sons, 1997.
3. D.H. ACKLEY, A connectionist machine for genetic hillclimbing, Kluwer Academic Publishers, 1987.
4. I. ALTHOFER, K.U. KOSCHNICK, On the convergence of "threshold accepting". Applied Mathematics and Optimization 24: 183-195, 1991.
5. E. ANGEL, V. ZISSIMOPOULOS. On the quality of local search for the quadratic assignment problem. Discrete Applied Mathematics 82: 15-25, 1998.
6. E. ANGEL, V. ZISSIMOPOULOS, Autocorrelation coefficient for the graph hipartitioning problem, Theoritical Computer Science 191: 229-243, 1998.
7. T. BÄCK, F. HOFFMEISTER, H-P. SCHWEFEL, A Survey of evolutionary strategies. Proc. of 4th Intl. Conference on Genetic Algorithms (ICGA'91). R. Belew, L. Booker (Eds.), Morgan Kaufmann, p. 2-9, 1991.
8. T. BÄCK, H-P. SCHWEFEL. An overview of evolutionary algorithms for parameter optimization. Evolutionay Computation 1(1): 1-24, 1993.
9. T. BÄCK, F. HOFFMEISTER, H.P. SCHWEFEL (Eds.), Applications of evolutionary algorithms. Report of the System Analysis Research Group (Sys), Univ. of Dortmund, ftp//lumpi.informatik.uni-dortmund.de/pub/EA/papers/ea-app.ps.gz, 1993.
10. T. BÄCK, Evolutionary algorithms in theory and practice. Oxford University Press, New York, 1995.
11. E. BALAS, W. NIEHAUS, Optimized crossover-based genetic algorithms tor the maximum cardinality and maximum weight clique problems. Journal of Heuristics 4: 107-122, 1998.
12. R.S. BARR, R.L. GOLDEN, J.P. KELLY, M.G.C. RESENDE, W.R. STEWART, Jr., Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics 1(1): 9-32, 1995.
13. R. BATTITI, M. PROTASI, Reactive search, a history-sensitive heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics 2, article 2, 1997.
14. J.E. BEASLEY, Langrangean relaxation. Dans C.R. Reeves (Ed.) Modern heuristics for combinatorial problems, Blackwell Scientific Publishing, Oxford, 1993.
15. M. BELAIDOUNI, J.K. HAO, Landscapes of the maximal constraint satisfaction problem. Artificial Evolution'99, Lecture Notes in Computer Science, Springer-Verlag, 1999.
16. P. BERTIER, B. ROY, Procédure de résolution pour une classe de problème pouvant avoir un caractère combinatoire. Cahiers du Centre d Etudes de Recherche Opérationnelle 6: 202-208, 1964.
17. J.R. BITNER, E.M. REINGOLD, Backtracking programming techniques. Communications of the ACM 18(11): 651-656, 1975.
18. E. BONOMI, J.L. LUTTON, The N-city travelling salesman problem, statistical mechanics and the metropolis algorithm, SIAM Review 26(4): 551-568, 1984.
19. D. BRÉLAZ. New methods to color vertices of a graph. Communications of ACM 22: 251-256, 1979.
20. A. CAMINADA, Résolution du problème de l'affectation des fréquences par programmation par contraintes. Note Technique FT.CNET/BEL/POH/CDI/71-95/CA. CNET Belfort, 1995.
21. D.J. CASTELINO, S. HURLEY, N.M. STEPHENS. A tabu search algorithm for frequency assignment. G. Laporte, I. H. Osman, (Eds.), Metaheuristics in Combinatorial Optimization, Annals of Operations Research 63: 301-319, 1996.
22. R. CERF, Une théorie asymptotique des algorithmes génétiques, Thèse de Doctorat, Université de Montpellier II, 1994.
23. V. CERNY. A thermodynamical approach to the travelling salesman problem: an efficient simulated annealing algorithm. Journal of Optimization Theory and Applications 45: 41-51, 1985.
24. M. CHAMS, A. HERTZ, D. de WERRA, Some experiments with simulated annealing for coloring graphs. EJOR 32: 260-266, 1987.
25. I. CHARON, O. HUDRY, The noising method: a new method for combinatorial optimization. Operations Research Letters 14: 133-137, 1993.
26. I. CHARON, O. HUDRY, Mixing different component of metaheuristics. I.H. Osman, J.P. Kelly (Eds.), Metaheuristics. Theory and Applications, Kluwers Academic Publishers, p. 589-604, 1995.
27. P. CHEESEMAN, B. KANEFSKY, W.M. TAYLOR, Where the really hard problems are? Proc. of IJCAI'91, Sydney, Springer-Verlag, p. 331-337, 1991.
28. N.E. COLLINS, R.W. EGLESE, B.L. GOLDEN, Simulated annealing: an annotated bibliography. American Journal of Mathematical and Management Sciences 8(3-4): 209-307, 1988.
29. A. COLMERAUER, An introduction to Prolog III, Communications of the ACM 33(7): 69-90, 1990.
30. D. CONNOLLY, An improved annealing scheme for the QAP. European Journal of Operational Research 46: 93-100, 1990.
31. S.A. COOK, The complexity of theory-proving procedures. Proc. of 3rd Annual ACM Symposium on Theory of Computing. ACM, Ney York, p. 151-158, 1971.
32. D. COSTA, A. HERTZ, O. DUBUIS. Embedding of a sequential procedure within an evolutionary algorithms for coloring problems in graphs. Journal of Heuristics 1(1): 105-128, 1995.
33. P. CRESCENZI, V. KANN, A compendium of NP-optimization problems. Research Report, University La Sapienza, Roma, 1996.
34. W. CROMPTON, S. HURLEY, N.M. STEPHENS, A parallel genetic algorithm for frequency assignment problems. Proc. of IMACS SPRANN'94, p. 81-84, 1994.
35. L. DAVIS, Handbook of genetic algorithms, Van Nostrand Reinhold, New York, 1991.
36. M. DINCBAS, P. VAN HENTENRYCK, H. SIMONIS, A. AGGOUN, T GRAF, F. BERTHIER, The constraint logic programming language CHIP. Proc. of Intl. Conf. on Fifth Generation Computer Systems, p. 693-702, Tokyo, Japan, 1988.
37. K.A. DE JONG, W. SPEARS, On the state of evolutionary computation. Proc. of International Conference on Genetic Algorithms (ICGA'93), p. 618-623, 1993.
38. K.A. DE JONG, Genetic algorithms : A 25 year perspective. Dans J.M. Zurada, R.J. Marks II, C.J. Robinson (Eds.), Computional Intelligence-Imitating Life, IEEE Press, p. 125-134, 1994.
39. U. DORNDORF, E.PESCH, Fast clustering algorithms. ORSA Journal on Computing 6: 141-153, 1994.
40. R. DORNE, J.K. HAO, An evolutionary approach for frequency assignment in cellular radio networks. Proc. of IEEE Intl. Conf. on Evolutionary Compulation (ICEC'95), p. 539-544. Perth, Australie, 1995.
41. R. DORNE, J.K. HAO, Constraint handling in evolutionary search: A case study on frequency assignment. Lecture Notes in Computer Science 1141: 801-810. Springer-Verlag, 1996.
42. R. DORNE, J.K. HAO, A new genetic local search algorithm for graph coloring. Lecture Notes in Computer Science 1498: 745-754, Springer-Verlag, 1998.
43. M. DORIGO, G. DI CARO, The ant colony optimization meta-heuristic. Dans D. Corne, M. Dorigo, F. Glover (Eds.), New Ideas in Optimization, McGraw-Hill, 1999.
44. G. DUCEK, T. SCHEUER, Threshold accepting, a general purpose optimization algorithm. Journal of Computational Physics 90: 161-175, 1990.
45. G. DUCEK, New optimization heuristics: the great deluge algorithm and the record-to-record travel. Journal of Computational Physics 104 :86-92, 1993.
46. M. DUQUE-ANTÒN, D. KUNZ, B. RÜBER, Channel assignment for cellular radio using simulated annealing. IEEE Trans. on Vehicular Technology 42 :14-21, 1993.
47. R. EGLESE, Heuristics in operational research. Dans V. Belton, B. O'keefe (Eds.), Recent Developments in Operational Research, Pergamon Press, 1986.
48. R.W. EGLESE, Simulated annealing: a tool for operational research. European Journal of Operational Research 46: 271-281, 1990.
49. U. FAIGLE, W. KERN. Some convergence results for probabilistic Tabu Search. ORSA Journal on Computing 4: 32-37, 1992.
50. E. FALKENAUER, A hybrid grouping genetic algorithm for bin packing. Journal of Heuristics 2(1): 5-30, 1996.
51. H. FARGIER, D. DUBOIS, H. PRADE, Problèmes de satisfaction de contraintes flexibles : une approche égalitarisle, Revue d'Intelligence Artificielle 9: 311-354, 1995.
52. T.A. FEO, M.G.C. RESENDE, A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8: 67-71, 1989.
53. T.A. FEO, M.G.C. RESENDE, S.H. SMITH, A greedy randomized adaptive search procedure for maximum independent set. Operations Research 42: 860-878, 1994.
54. T.A. FEO, M.G.C. RESENDE, Greedy randomized adaptive search procedures. Journal of Global Optimization 6: 109-133, 1995.
55. M.L. FISHER, A.H.G. RINNOY KAN, The Design, analysis and implementation of heuristics. Management Science 34: 263-265, 1989.
56. C. FLEURENT, J.A. FERLAND, Object-oriented implementation of heuristic search methods for graph coloring, maximum clique, and satisfiability. Dans [JOH 96], p. 619-652, 1996.
57. C. FLEURENT, J.A. FERLAND, Genetic and hybrid algorithms for graph coloring. Dans G. Laporte, I.H. Osman, (Eds.), Metaheuristics in Combinatorial Optimization. Annals of Operations Research, 63: 437-441, 1996.
58. L.J. FOGEL, A.J. OWENS, M.J. WALSH, Artificial intelligence through simulated evolution, Wiley, New York, 1966.
59. D.B. FOGEL, J.W. ATMAR. Comparing genetic operators with gaussian mutations in simulted evolutionary prosesses using linear systems. Biol. Cybern 63: 111-114, 1990.
60. D.B. FOGEL, Evolutionary programming: an introduction and some current directions. Statistics and Computing 4: 11-129, 1994.
61. B. FREISLEBEN, P. MERZ, New genetic local search operators for the traveling saleman problem Lecture Notes in Computer Science 1141: 890-899. Springer-Verlag. 1996.
62. E.C. FREUDER, R.J. WALLACE, Partial constraint satisfaction. Journal of Artificial Intelligence 58(1-3): 21-70, 1992.
63. N. FUNABIKI, Y. TAKEFUJI, A neural network parallel algorithm for channel assignment problems in cellular radio network. IEEE Transactions on Vehicular Technology 41: 430-437, 1992.
64. P. GALINIER, J.K. HAO, Tabu search for maximal constraint satisfaction problems. Lecture Notes in Computer Science 1330: 196-208, Springer-Verlag, 1997.
65. P. GALINIER, J.K. HAO, Hybrid evolutionary algorithms for graph coloring, à paraître dans Journal of Combinatorial Optimization, 1999.
66. P. GALINIER, Etude des métaheuristiques pour la résolution du problème de satisfaction de contraintes et de la coloration de graphes. Thèse de Doctorat. Université de Montpellier II, janvier 1999.
67. A. GAMST, W. RAVE. On the frequency assignment in mobile automatic telephone systems. Proc. of GLOBECOM'92, p. 309-315, 1982.
68. A. GAMST, Some lower bounds for a class of frequency assignment problems. IEEE Transactions on Vehicular Technology 35: 8-14, 1986.
69. M.R. GAREY, D.S. JOHNSON, Computers and intractability: a guide to the theory of NP-completeness, W.H. Freeman and Company, New York, 1979.
70. F. GLOVER, A multiphase-dual algorithm for the zero-one integer programming problem. Operations Research 13: 879-919, 1965.
71. F. GLOVER, Heuristics for integer programming using surrogate constraints. Decision Sciences 8: 156-166, 1977.
72. F. GLOVER, Future paths for integer programming and links to artificial intelligence. Computers and Operations Research 13: 533-549, 1986.
73. F. GLOVER, Tabu search: part I. ORSA J. on Computing 1(3): 190-206, 1989.
74. F. GLOVER, Tabu Search; Part II. ORSA J. on Computing 2(1): 4-32, 1989.
75. F. GLOVER, M. LAGUNA. Tabu Search, Dans C.R. Reeves (Ed.) Modern heuristics for combinatorial problems. Blackwell Scientific Publishing, Oxford, 1993.
76. F. GLOVER, E. TAILLARD, M. LAGUNA, D. de WERRA (Eds.), Tabu search. Special Issue of Annals of Operations Research 41, 1993.
77. F. GLOVER, J.P. KELLY, M. LACUNA. Genetic algorithms and tabu search -hybrids for optimization. Computers & Operations Research 22: 111-?, 1995.
78. F. GLOVER, M. LACUNA, Tabu search, Kluwer Academic Publishers, 1997.
79. F. GLOVER, A template for scatter search and path relinking. Dans J.K. Hao, E. Lutton, E. Ronald, M. Schoenauer, D. Snyers (Eds.), Artificial Evolution'97, Lecture Notes in Computer Sciences 1363: 3-51, Spinger-Verlag, 1998.
80. D.E. GOLDBERG, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, New York, 1989.
81. M.X. GOEMANS, D.P. WILLIAMSON, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of ACM 42: 1115-1145, 1995.
82. J.J. GREFENSTETTE, Incorporating problem specific knowledge into genetic algorithms. Dans L. Davis (Ed.) Genetic Algorithms and Simulated Annealing, Morgan Kaufmann Publishers, p. 42-60, 1987.
83. B. HAJEK, Cooling schedules for optimal annealing. Mathematics of Operations Research 13: 311-329, 1988.
84. W.K. HALE, Frequency assignment: theory and application. Proceedings of the IEEE 68(12): 1498-1573, 1980.
85. P. HANSEN, The steepest ascent mildest descent heuristic for combinatorial programming. Congress on Numerical Methods in Combinatorial Optimization, Capri, Italie, 1986.
86. P. HANSEN, B. JAUMARD, Algorithms for the maximum satisfiability problem. Computing 44: 279-303, 1990.
87. J.K. HAO, R. DORNE, Study of genetic search for the frequency assignment problem. Lecture Notes in Computer Science 1063: 333-344, Springer-Verlag, 1996.
88. J.K. HAO, R. DORNE, Empirical studies of heuristic local search for constraint solving. Lecture Notes in Computer Science 1118: 194-208, Springer-Verlag, 1996.
89. J.K. HAO, R. DORNE, P. GALINIER, Tabu search for frequency assignment in mobile radio networks. Journal of Heuristics 4(1): 47-62, 1998.
90. J. HART, A. SHOGAN, Semi-greedy heuristics: an empirical study. Operations Research Letters 6: 107-114, 1987.
91. M. HELP, R.M. KARP, The travelling-saleman problem and minimum spanning trees. Operations Research 18: 1138-1162, 1970.
92. M. HELP, R.M. KARP, The travelling-saleman problem and minimum spanning trees: part II. Mathematical Programming 1: 6-25, 1971.
93. A. HERTZ, D. De WERRA, Using Tabu search techniques for graph coloring. Computing 39: 345-351, 1987.
94. A. HERTZ, B. JAUMARD, M.P. DE ARAGAO. Local Optima Topology for the k-coloring Problem. Discrete Applied Mathematics 49: 257-280, 1994.
95. J.H. HOLLAND, Adaptation in natural and artificial systems. The University of Michigan Press. Ann Arbor, 1975.
96. J.H. HOLLAND, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT Press/Bradford Books, 2nd Edition. Cambridge, MA, 1992.
97. J.N. HOOKER. Needed: An empirical science of algorithms. Operations Research 42: 201-212, 1994.
98. J.N. HOOKER, Testing heuristics: we have it all wrong. Journal of Heuristics 1(1): 33-42 1995.
99. B.A. HUBERMAN, T. HOGG, C.P. WILLIAMS. Artificial Intelligence, Special Issue on Phase Transition and Complexity 82(1-2), 1996.
100. P. JOG, G. GUCHT, Parallelisation of probabilistic sequential search algorithms. Proc. of ICGA'87. San Mateo: Morgan Kaufmann, p. 170-176, 1987.
101. P. JOG, J. SUH, G. GUCHT, Parallel genetic algorithms applied to the traveling salesman problem. SIAM Journal on Optimization 1(4): 515-529, 1991.
102. S. JOHNSON, C.H. PAPADIMITRIOU, M. YANNAKAKIS, How easy is local search? Journal of Computer and System Sciences 37(1): 79-100, 1988.
103. D.S. JOHNSON, C.R. ARAGON, L.A. MCGEOCH, C. SCHEVON, Optimization by simulated annealing: an experimental evaluation; Part I, graph partitioning. Operations Research 37: 865-892, 1989.
104. D.S. JOHNSON, C.R. ARAGON, L.A. MCGEOCH, C. SCHEVON, Optimization by simulated annealing: an experimental evaluation; Part II. graph coloring and number partitioning. Operations Research 39(3): 378-406, 1991.
105. D.S. JOHNSON, M.A. TRICK (Eds.), 2nd DIMACS implementation challenge -cliques, coloring and satisfiability. DIMACS Series in Discrete Mathematics and Theoretical Computer Science 26, American Mathematical Society, 1996.
106. T. JONES, S. FORREST, Fitness distance correlation as a measure of problem difficulty for genetic algorithms. Proceedings of the 6th International Conference on Genetic Algorithms, 184-192, 1995.
107. S.A. KAUFFMAN, Adaptation on rugged fitness landscapes. Dans D. Stein (Ed.). Lectures in the Sciences of Complexity. Addison-Wesley Longman, 527-618, 1989.
108. M. KARRAY, A. ORTEGA, J-M. RAIBAUD, Etudes d'algorithmes de recherche de cliques et de coloration des graphes. Note Technique NT/PAB/SRM/RRM/4027, CNET Paris, 1995.
109. W. KERN, On the depth of combinatorial optimization problems. Discrete Applied Math. 43: 115-129, 1993.
110. B.W. KERNIGHAN, S. LIN, An efficient heuristic for partitioning graphs. Bell System Technology Journal 49(2): 291-307, 1970.
111. S. KIRKPATRICK, C.D. GELATT, P.M. VECCHI, Optimization by simulated annealing. Science 220: 671-680, 1983.
112. A. KOLEN, E. PESCH, Genetic local search in combinatorial optimization, Discrete Applied Mathematics. 48: 273-, 1994.
113. K. KONOLIGE, Easy to be hard: Difficult problems for greedy algorithms. Proc. of 4th Intl. Conf. on Principles of Knowledge Representation and Reasoning, p. 374-378, 1994.
114. G. KONTORAVDIS, J.F. BARD. Improved heuristics for the vehicle routing problem with time windows. ORSA Journal on Computing 7, 1995.
115. B. KORTE, L. LOVASZ, R. SCHRADER, GREEDOIDS, Algorithms and Combinatorics 4, Springer Verlag, 1991.
116. C. KOULAMAS, S.R. ANTHONY, R. JEAN, A survey of simulated annealing application to operations research problems. OMEGA 22: 41-56, 1994.
117. M. KUBALE, Interval vertex-coloring of a graph with forbidden colors. Discrete Mathematics 74: 125-136, 1989.
118. D. KUNZ. Channel assignment for cellular radio using neural networks. IEEE Trans. on Vehicular Technology 40: 88-193, 1991.
119. M. LAGUNA, T.A. FEO, H.C. ELROD, A greedy randomized adaptive search procedure for the two-partition problem. Operations Research 42: 677-687, 1994.
120. E.L. LAWLER, D.E. WOOD, Branch and bound methods: a survey. Operations Research 14: 699-719, 1996.
121. G. LAPORTE, I.H. OSMAN, Metaheuristics in combinatorial optimization. Annals of Operations Research 63, J.C. Baltzer Science Publishers, Basel, Switzerland, 1996.
122. J-L. LAURIÈRE, A language and a program for stating and solving combinatorial problems. Artificial Intelligence 10: 29-127, 1978.
123. F.T. LEIGHTON, A graph coloring algorithm for large scheduling problems. Journal of Research of the National Bureau Standard 84: 489-505, 1979.
124. S. LIN, Computer solutions of the travaleing salesman problem. Bell System Technology Journal 44(10): 2245-2269, 1965.
125. S. LIN, B.W. KERNIGHAN, An efficient heuristic for the traveling-salesman problem. Operations Research 21: 498-516, 1973.
126. F.T. LIN, C.Y. KAO, C.C. HSU, Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Systems. Man and Cybernetics 23: 1752-1767, 1993.
127. S. LUNDY, A. MEES, Convergence of an annealing algorithm. Mathematical Programming 34: 111-124, 1986.
128. A.K. MACKWORTH, Consistency in networks of relations. Journal of Artificial Intelligence 8(1): 99-118, 1977.
129. A.K. MACKWORTH, Constraint satisfaction, Dans S.C. Shapiro (Ed.) Encyclopedia on Artificial Intelligence, John Wiley & Sons, NY, 1987.
130. S.W. MAHFOUD, D.E. GOLDBERG, Parallel recombinative simulated annealing: a genetic algorithms. Parallel Computing 21:1 -28, 1995.
131. W. METROPOLIS, A. ROENBLUTH, M. ROSENBLUTH, A. TELLER, E. TELLER, Equation of the state calculations by fast computing machines. Journal of Chemical Physics 21: 1087-1092, 1953.
132. Z. MICHALEWICZ, Genetic Algorithms + data structures = evolution programs, Springer Verlag, Berlin, 1992.
133. S. MINTON, M.D. JOHNSTON, P. LAIRD, Solving large scale constraint satisfaction and scheduling problems using a heuristic repair method. Proc. of AAAI'90. Boston, MA, 1990.
134. S. MINTON, M.D. JOHNSTON. P. LAIRD, Minimizing conflicts : A heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence 58(1-3): 161-206, 1992.
135. P. MORRIS, The breakout method for escaping from local minima. Proc. of AAAI'93. 1993.
136. C. MORGENSTERN, Distributed coloration neighborhood search. Dans [JOH 96], p. 335-358, 1996.
137. P. MOSCATO, On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program. C3P Report 826, 1989.
138. P. MOSCATO, An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search. Annals of Operations Research 41: 85-122, 1993.
139. R. MOTVANI, Lecture notes on approximation algorithms. Volume I. Research Report Standford University, 1995.
140. H. MÜLHENBEIN, M. GORGES-SCHLEUTER, O. KRÄMER, Evolution algorithms in combinatorial optimization Parallel Computing 7: 65-85, 1988.
141. H. MÜLHENBEIN, Evolutionary algorithms: theory and application. Dans E.H.L. Aarts, J.K. Lenstra (Eds.) Local search in combinatorial optimization, John Wiley & Sons, Chichester, 1995.
142. T. NICHOLSON, Optimization techniques. dans Optimization in Industry, Longman Press, London. 1971.
143. A. ORTEGA, J.M. RAIBAUD, M. KARRAY, M. MARZOUG, A. CAMINADA, Algorithmes de coloration des graphes et d'affectation des fréquences. Note Technique, NT/PAB/SRM/ RRM/4353, CNET Paris. Août 1995.
144. I.H. OSMAN, An introduction to meta-heuristics. Dans M. Lawerence, C. Wilson (Eds.) Operational Research Papers, ORS Press, Birmingham, U.K., 1995.
145. I.H. OSMAN, J.P. KELLY (Eds.), Meta-heuristics: theory and applications, Kluwers Academic Publishers, Boston, 1996.
146. C.H. PAPADIMITRIOU, K. STEIGLITZ, Combinatorial optimization - algorithms and complexity. Prentice Hall, 1982.
147. C.H. PAPADIMITRIOU, Computional complexity. Addison-Wesley, 1994.
148. J. PEARL, Heuristics, Addison-Wesley, Reading, MA. 1984.
149. J.P. PUGET, A C++ implementation of CLP, Prof. of Singapore Intl. Conf. on Intelligent Systems, 1994.
150. N.J. RADCLIFF, P.D. SURRY, Fundamental limitations on search algorithms: evolutionary computing in perspective. Lecture Notes in Computer Science 1000, Springer-Verlag, 1995.
151. R.L. RARDIN, M. SUDIT, Paroidal search: genetic local combinatorial optimization. Discrete Applied Math. 43: 155-174, 1993.
152. I. RECHENBERG, Evolutionasstrategie : Optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog, Stuttgart, 1973.
153. C.R. REEVES (Ed.) Modern heuristic techniques for combinatorial problems, Blackwell Scientific Publications, Oxford, 1993.
154. C.R. REEVES, J. BEASLEY, Introduction. Dans C.R. Reeves (Ed.) Modern heuristic techniques for combinatorial problems, Blackwell Scientific Publications, Oxford, 1993.
155. C.C. RIBEIRO, N. MACULAN (Eds.), Applications of combinatorial optimization. Annals of Operations Research 50, 1994.
156. A.A. SCHAFFER, M. YANNAKAKIS, Simple local search problems that are hard to solve. SIAM J. Computing 20(1): 56-87, 1991.
157. T. SCHIEX, H. FARGIER, G. VERFAILLIE, Problèmes de satisfaction de contraintes valués, Revue d'Intelligence Artificielle 11(3): 339-373, 1997.
158. M. SCHOENAUER, Z. MICHALEWICZ. Evolutionary computation: an introduction. Control and Cybernetics 26(3): 307-338, 1997.
159. H.P. SCHWEFEL, Numerical optimization of computer models, John Wiley & Sons, 1981.
160. B. SELMAN, H.J. LEVESQUE, M. MITCHELL, A new method for solving hard satisfiability problems. Proc. of AAAI'92. San Jose, CA, p. 440-446, 1992.
161. B. SELMAN, H.A. KAUTZ, Domain-independent extensions to GSAT : solving large structured satisfiability problems. Proc. of IJCAI'93. Chambéry, p. 46-51, 1993.
162. B. SELMAN, H.A. KAUTZ, C. BRAM, Noise strategies for improving local search. Proc. of AAAI'94, Seattle, WA, 1994.
163. S.C. SHAPIRO (Ed.), Encyclopedia of artificial intelligence. John Wiley & Sons, NY, 1987.
164. A. SILVER, R.V.V. VIDAL, D. de WERRA, A tutorial on heuristic methods. European Journal of Operational Research 5: 153-162, 1980.
165. M. SINCLAIR, Comparison of the performance of modern heuristics for combinatorial problem on real data. Computers & Operations Research 20: 687-695, 1993.
166. W.M. SPEARS, Simulated annealing for hard satisfiability problems. Dans [JOH 96], p. 533-558, 1996.
167. P.F. STADLER, Correlation in landscapes of combinatorial optimization problems. Europhys. Letters 20: 479-482, 1992.
168. P.F. STADLER. Landscapes and their correlation functions. Journal of Math. Chem. 20: 1-45, 1996.
169. R.E. STEARNS, H.B. HUNT III, Power indices and easier hard porblem. Math. Syst. Theory 23: 209-225, 1990.
170. th Allerton Conf. on Circuit and System Theory, Urbana, Illinois, p. 814-921, 1968
171. E-G. TALBI, Z. HAFIDI, J-M. GEIB, Parallel adaptive tabu search for large optimization problems, à paraître dans Parallel Computing.
172. E. TSANG, Foundations of constraint satisfaction, Academic Press, 1993.
173. N.L.J. ULDER, E.H.L. AARTS, H.J. BANDELT, P.J.M. van LAARHOVEN, E. PESCH, Genetic local search algorithms for the traveling salesman problem. Lecture Notes in Computer Science 496, 1991.
174. P.J. van LAARHOVEN, E.H.L. AARTS, Simulated annealing: theory and applications, Reidl, Dordrecht, 1987.
175. M.G.A. VERHOEVEN, E.H.L. AARTS, Parallel local search. Journal of Heuristics 1(1): 43-65, 1995.
176. R.V. VIDAL (Ed.) Applied simulated annealing, Lectures Notes in Economics and Mathematical Systems 396 : Springer-Verlag, Berlin, 1993.
177. C. VOUDOURIS, E. TSANG, Guided local search. TR CSM-247, University of Essex. UK, 1995.
178. R. WALLACE, Analysis of heuristic, methods for partial constrait satisfaction problem. Lecture Notes in Computer Science 1118: 482-486, Springer-Verlag, 1996.
179. W. WANG, C.K. RUSHFORTH, An adaptive local-search algorithm for the channel assignment problem (CAP). IEEE Trans. Vehicular Tech. 45: 459-466, 1996.
180. E. WEINBERGER, Correlated and uncorrelaled fitness landscape and how to tell the difference. Biol. Cybern. 63: 325-336, 1990.
181. D.H. WOLPERT, W. G. MACREADY, No free lunch theorems for optimization. IEEE Transactions On Evolutionary Compulation 1(1): 67-82, 1997.
182. M. YANNAKAKIS, The analysis of local search problems and their heuristics. Proc. of 7th Symposium on Theoritical Aspects of Computer Science. Lecture Notes in Computer Science 415: 298-311, Springer-Verlag, 1990.
183. M. YANNAKAKIS, Computational complexity of local search. Dans E.H.L. Aarts, J.K. Lenstra (Eds.), Local Search in Combinatorial Optimization, John Wiley & Sons, Chicester, 1995.
184. N. YUGAMI, Y. OHTA, H. HARA, Improving repair-based constraint satisfaction methods by value propagation. Proc. of AAAI'94, Seattle, WA, p. 344-349, 1994.
185. H. S. ZANAKIS, J.R. EVANS, A.A. VAZACOPOULOS, Heuristic methods and applications: a categorized survey. European Journal of Operational Research 43: 88-110, 1989.