Hybridizing exact methods and metaheuristics: A taxonomy

European Journal of Operational Research

Volumn 199, Issue 3, 2009, Pages 620-629

  Jourdan, L ^a   Basseur, M ^a   Talbi, E G ^a  

^a ECOLE CENTRALE DE LILLE   (France)

Author keywords

Combinatorial optimisation; Exact methods; Metaheuristics; Taxonomy

Indexed keywords

COMBINATORIAL OPTIMISATION; COOPERATIVE SCHEMES; EXACT METHODS; HEURISTIC APPROACH; HYBRID METHOD; HYBRID OPTIMIZATION METHOD; METAHEURISTICS;

COMBINATORIAL OPTIMIZATION; TAXONOMIES;

HEURISTIC METHODS;

EID: 67349158758     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2007.07.035     Document Type: Article

References (87)

1. E. Alba, F. Almeida, M. Blesa, C. Cotta, M. Dfaz, I. Dorta, J. Abarr, J. Gonzlez, C. Len, L. Moreno, J. Petit, J. Roda, A. Rojas, F. Xhafa, Mallba: A library of skeletons for combinatorial optimisation, in: Proceedings of the Euro-Par, volume LNCS 2004, 2002, pp. 927-932.
2. Augerat P., Belenguer J.M., Benavent E., Corber'n A., and Naddef D. Separating capacity constraints in the CVRP using tabu search. European Journal of Operational Research 106 2-3 (1998) 546-557
3. D. Applegate, R. Bixby, V. Chvátal, W. Cook, Finding tours in the TSP. Technical Report 99885, Forschungsinstitut fnr Diskrete Mathematik, Universität Bonn, Germany, 1999.
4. Ahuja R.K., Ergun O., Orlin J.B., and Punnen A.P. A survey of very large-scale neighborhood search techniques. Discrete Applied Mathematics 123 1-3 (2002) 75-102
5. Androulakis I., Maranas C., and Floudas C. ffbb: A global optimization method for general constrained nonconvex problems. Journal of Global Optimization 7 (1995) 337-363
6. Atamturk A., Nemhauser G.L., and Savelsbergh M.W.P. A combined lagrangian, linear programming and implication heuristic for large-scale set partitioning problems. Journal of Heuristics 1 (1996) 247-259
7. Alvim A.C.F., Ribeiro C., Glover F., and Aloise D.J. A hybrid improvement heuristic for the one-dimensional bin packing problem. Journal of Heuristics 10 2 (2004) 205-229
8. Burke E.K., Cowling P.I., and Keuthen R. Effective local and guided variable neighbourhood search methods for the asymmetric travelling salesman problem. Applications of Evolutionary Computing. EvoWorkshops2001. Lecture Note in Computer Science vol. 2037 (2001), Springer-Verlag, Como, Italy 203-212
9. Barake M., Chardaire P., and McKeown G.P. Metaheuristics: Computer decision-making. The Probe Metaheuristic and its Application to the Multiconstraint Knapsack Problem (2004), Kluwer Academic Publishers, Norwell, MA, USA
10. Bennell J.A., and Dowsland K.A. Hybridising tabu search with optimisation techniques for irregular stock cutting. Management Science 47 8 (2001) 1160-1172
11. Büdenbender K., Grünert T., and Sebastian H.-J. A hybrid tabu search/branch-and-bound algorithm for the direct flight network design problem. Transportation Science 34 4 (2000) 364-380
12. Barnhart C., Johnson E.L., Nemhauser G.L., Savelsbergh M.W.P., and Vance P.H. Branch-and-price: Column generation for solving huge integer programs. Operations Research (1998) 316-329
13. Bard J.F., Kontoravdis G., and Yu G. A branch-and-cut procedure for the vehicle routing problem with time windows. Transportation Science 36 (2002) 250-269
14. Basseur M., Lemesre J., Dhaenens C., and Talbi E.-G. Cooperation between branch and bound and evolutionary approaches to solve a biobjective flow shop problem. Workshop on Evolutionary Algorithms (WEA'04) vol. 3059 (2004), Springer-Verlag 72-86
15. Blum C. Beam-aco - hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers and OR 32 (2005) 1565-1591
16. R. Bent, P. Van Hentenryck, A two-stage hybrid local search for the vehicle routing problem with time windows. Technical Report Tech Report CS-01-06, Brown University, USA, September 2001.
17. Cotta C., Aldana J.F., Nebro A.J., and Troya J.M. Hybridizing genetic algorithms with branch and bound techniques for the resolution of the tsp. In: Pearson D.W., Steele N.C., and Albrecht R.F. (Eds). Artificial Neural Nets and Genetic Algorithms 2 (1995), Springer-Verlag 277-280
18. Cortinhal M.J., and Captivo M.E. Upper and lower bounds for the single source capacitated location problem. EJOR 151 2 (2003) 333-351
19. D. Clements, J. Crawford, D. Joslin, G. Nemhauser, M. Puttlitz, M. Savelsbergh, Heuristic optimization: a hybrid AI/OR approach, in: Workshop on Industrial Constraint-Directed Scheduling, Schloss Hagenberg, Austria, 1997.
20. A. Chabrier, E. Danna, C. Le Pape, Coopération entre génération de colonnes sans cycle et recherche locale appliquée au routage de véhicules, in: JNPC, 2002.
21. Crainic T.G., Gendreau M., and Farvolden J.M. A simplex-based tabu search method for capacitated network design. INFORMS Journal on Computing 12 3 (2000) 223-236
22. Cowling P.I., and Keuthen R. Embedded local search approaches for routing optimization. Computers and OR 32 (2005) 465-490
23. Y. Caseau, F. Laburthe, Effective forget-and-extend heuristics for scheduling problems, in: CP-AI-OR'02 - Fourth International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 1999, Ferrara (Italy).
24. Congram R.K., Potts C.N., and van de Velde S.L. An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problem. INFORMS Journal on Computing 14 1 (2002) 52-67
25. Chelouah R., and Siarry P. A hybrid method combining continuous tabu search and nelder-mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research 161 3 (2005) 636-654
26. Cotta C., and Troya J.M. Embedding branch and bound within evolutionary algorithms. Applied Intelligence 18 (2003) 137-153
27. Dahal K.P., Aldridge C.J., and Galloway S.J. Evolutionary hybrid approaches for generation scheduling in power systems. European Journal of Operational Research (2006) [Available online, 25 January 2006]
28. De Backer B., Furnon V., Kilby P., Prosser P., and Shaw P. Solving vehicle routing problems using constraint programming and metaheuristics. Journal of Heuristics 6 4 (2000) 501-524
29. Doerner K.F., Gutjahr W.J., Hartl R.F., Strauss C., and Stummer C. Pareto ant colony optimization with ilp preprocessing in multiobjective project portfolio selection. European Journal of Operational Research 171 3 (2006) 830-841
30. Della Croce F., Ghirardi M., and Tadei R. Recovering beam search: Enhancing the beam search approach for combinatorial optimization problems. Journal of Heuristics 10 1 (2004) 89-104
31. M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy. Technical Report No. 91016, Dipartimento di Elettronica e Informatica, Politecnico di Milano, Italie, 1991.
32. J. Denzinger, T. Offermann, On cooperation between evolutionary algorithms and other search paradigms, in: CEC-99, 1999, pp. 2317-2324.
33. C. Duhamel, A. Quilliot, Coupling a metaheuristic with an exact method for the local access network design problem, In Optimization 2001, 2001.
34. I. Dumitrescu, T. Stützle, Combinations of local search and exact algorithms, in: EvoWorkshops 2003, 2003, pp. 211-223.
35. Feltl H., and Raidl G.R. An improved hybrid genetic algorithm for the generalized assignment problem. Symposium on Applied Computing, SAC'04 (2004), ACM Press 990-995
36. French A.P., Robinson A.C., and Wilson J.M. Using a hybrid genetic-algorithm/branch and bound approach to solve feasibility and optimization integer programming problems. Journal of Heuristics 7 6 (2001) 551-564
37. Gallardo J.E., Cotta C., and Fernández A.J. Solving the multidimensional knapsack problem using an evolutionary algorithm hybridized with branch and bound. In: Mira J., and Álvarez J.R. (Eds). IWINAC (2). Lecture Notes in Computer Science vol. 3562 (2005), Springer 21-30
38. Gomes da Silva C., Figueira J., and Climaco J. Integrating partial optimization with scatter search for solving bi-criteria 0,1-knapsack problems. European Journal of Operational Research (2006) (Available online, 17 February 2006)
39. Glover F. Heuristics for integer programming using surrogate constraints. Decision Sciences 8 1 (1977) 56-166
40. Glover F. Future paths for integer programming and links to artificial intelligence. Computers and Operations Research 13 (1986) 533-549
41. Gomes A.M., and Oliveira J.F. Solving irregular strip packing problems by hybridising simulated annealing and linear programming. European Journal of Operational Research 171 3 (2006) 811-829
42. Gomory R. Outline of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society 64 (1958) 275
43. Haouari M., and Ladhari T. A branch-and-bound-based local search method for the flow shop problem. Journal of the Operational Research Society 54 10 (2003) 1076-1084
44. Holland J. Adaptation in Natural and Artificial Systems (1975), University of Michigan Press
45. Jahuira C.A.R. Hybrid genetic algorithm with exact techniques applied to tsp. Second International Workshop on Intelligent Systems Design and Application (2002), Dynamic Publishers 119-124
46. C.A.R. Jahuira, E. Cuadros-Vargas. Solving the tsp by mixing gas with minimal spanning tree, in: Ernesto Cuadros-Vargas, Eduardo Tejada-Gamero, Adenilso da Silva Simao, (Eds.), First International Conference of the Peruvian Computer Society, Lima - Perù, Peruvian Computer Society, January 2003, pp. 123-123.
47. P. Koduru, S. Das, S. Welch, J.L. Roe, A multi-objective ga-simplex hybrid approach for gene regulatory network models, in: Congress on Evolutionary Computation (CEC'04), vol. 2, IEEE Service Center, Portland, Oregon, USA, June 2004, pp. 2084-2091.
48. J.O. Kephart, A biologically inspired immune system for computers, in: Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, Cambridge, MA, US, MIT Press, 1994, pp. 130-139.
49. Kostikas K., and Fragakis C. Genetic programming applied to mixed integer programming. EuroGP 2004. Lecture Notes in Computer Science vol. 3003 (2004) 113-124
50. Kirkpatrick S., Gelatt D.C., and Vechhi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
51. G.W. Klau, I. Ljubic, A. Moser, P. Mutzel, P. Neuner, U. Pferschy, G. Raidl, R. Weiskircher, Combining a memetic algorithm with integer programming to solve the prize-collecting Steiner tree problem, in: K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P. Lanzi, L. Spector, A. Tettamanzi, D. Thierens, A. Tyrrell, (Eds.), Lecture Note in Computer Science, Proceedings of the 2004 Genetic and Evolutionary Computation Conference, vol. 3102, Seattle, Washington, USA, June 2004, pp. 1304-1315.
52. Koza J.R. Genetic Programming: On the Programming of Computers by Natural Selection (1992), MIT Press, Cambridge, MA
53. Klepeis J.L., Pieja M.J., and Floudas C.A. Hybrid global optimization algorithms for protein structure prediction: Alternating hybrids. Biophysical Journal 4 84 (2003) 869-882
54. Lawler E.L. Combinatorial Optimization: Networks and Matroids (1976), Holt, Reinhart, and Winston, New York
55. Lin Z.-Z., Bean J.C., and White III C.C. A hybrid genetic/optimization algorithm for finite-horizon, partially observed markov decision processes. INFORMS Journal on Computing 16 1 (2004) 27-38
56. Lourenço H.R., Martin O., and Stützle T. Handbook of Metaheuristics. Iterated Local Search (2002), Kluwer Academic Publishers, Norwell, MA
57. Lee J., Scheraga H., and Rackovsky S. New optimization method for conformational energy calculations on polypeptides: Conformational space annealing. Journal of Computational Chemistry 18 (1997) 1222-1232
58. Lawler E.L., and Wood D.E. Branch and bound methods: A survey. Operations Research 14 (1966) 699-719
59. Maniezzo V. Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORM Journal on Computing 11 4 (1999) 358-369
60. T. Mautor, P. Michelon, Mimausa: A new hybrid method combining exact solution and local search, in: Second International Conference on Meta-Heuristics (MIC'97), 1997.
61. A. Marino, A. Prugel-Bennett, C. Glass, Improving graph colouring with linear programming and genetic algorithms, in: K. Miettinen, M.M. Makela, J. Toivanen (Eds.), EUROGEN99, Jyvaskyla, Finland, 1999, pp. 113-118.
62. Nwana V., Darby-Dowman K., and Mitra G. A co-operative parallel heuristic for mixed zero-one linear programming: Combining simulated annealing with branch and bound. European Journal of Operational Research 164 (2005) 12-23
63. Nagar A., Heragu S.S., and Haddock J. A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem. Annals of Operations Research 63 (1996) 397-414
64. Osman I.H., Hasan M., and Abdullah A. Lp-based meta-heuristics for the weighted maximal planar graph problem. Journal of Operational Research Society 53 10 (2002) 1142-1149
65. Palpant M., Artigues C., and Michelon P. LSSPER: Solving the resource-constrained project scheduling problem with large neighbourhood search. Annals of Operations Research 31 1 (2004) 237-257
66. Pesant G., and Gendreau P. A constraint programming framework for local search methods. Journal of Heuristics 5 3 (1999) 255-279
67. J. Puchinger, G.R. Raidl, An evolutionary algorithm for column generation in integer programming: An effective approach for 2d bin packing, In PPSN VIII, 2004, pp. 642-651.
68. J. Puchinger, G.R. Raidl, Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification, in: IWINAC, pp. 41-53, 2005.
69. Pardalos P.M., Ramakrishnan K.G., Resende M.G.C., and Li Y. Implementation of a variance reduction based lower bound in a branch and bound algorithm for the quadratic assignment problem. SIAM Journal on Optimization 7 (1997) 280-294
70. Papadimitriou C.H., and Steiglitz K. Combinatorial Optimization: Algorithms and Complexity (1982), Prentice-Hall
71. Plateau A., Tachat D., and Tolla P. A hybrid search combining interior point methods and metaheuristics for 0-1 programming. International Transactions in Operational Research 9 6 (2002) 731-746
72. Portmann M.C., Vignier A., Dardihac D., and Dezalay D. Branch and bound crossed with ga to solve hybrid flowshops. European Journal of Operational Research 107 2 (1998) 389-400
73. Portmann M.C., Vignier A., Dardilhac D., and Dezalay D. Branch and bound crossed with ga to solve hybrid flowshops. European Journal of Operational Research 107 (1998) 389-400
74. I. Rechenberg, Evolutions Strategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution, Frommann-Holzboog, Stuttgart, 1973.
75. G. Ribeiro Filho, L.A.N. Lorena, Constructive genetic algorithm and column generation: An application to graph coloring, in: APORS 2000 - The Fifth Conference of the Association of Asian-Pacific Operations Research Societies within IFORS, 2000.
76. Rosing C., and ReVelle C. Heuristic concentration: Two stage solution construction. European Journal of Operational Research 97 (1997) 75-86
77. Spina R., Galantucci L.M., and Dassisti M. A hybrid approach to the single line scheduling problem with multiple products and sequence-dependent time. Computers and Industrial Engineering 45 4 (2003) 573-583
78. Shaw P. Using constraint programming and local search methods to solve vehicle routing problems. Principles and Practice of Constraint Programming - CP98, 4th International Conference. Lecture Notes in Computer Science vol. 1520 (1998), Springer-Verlag, Pisa, Italy 417-431
79. Shi H., and Wang L. A mixed branch-and-bound and neural network approach for the broadcast scheduling problem. Design and Application of Hybrid Intelligent Systems. HIS'03 (2003), IOS Press 42-49
80. Talbi E.-G. A taxonomy of hybrid metaheuristics. Journal of Heuristics 8 2 (2002) 541-564
81. Talukdar S., Baerentzen L., Gove A., and de Souza P.S. Asynchronous teams: Cooperation schemes for autonomous agents. Journal of Heuristics 4 4 (1998) 295-321
82. Tamura H., Hirahara A., Hatono I., and Umano M. An approximate solution method for combinatorial optimization - a hybrid approach of genetic algorithm and Lagrange relaxation method. Transactions of the Society of Instrument and Control Engineers (Japan) 30 3 (1994) 329-336
83. T'kindt V., Monmarché N., Tercinet F., and Laügt D. An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operational Research 142 (2002) 250-257
84. Ulungu E.L., and Teghem J. The two phases method: An efficient procedure to solve bi-objective combinatorial optimization problems. Foundations of Computing and Decision Sciences 20 2 (1995) 49-165
85. S. Umetani, M. Yagiura, T. Ibaraki, One-dimensional cutting stock problem with a given number of setups: a hybrid approach of metaheuristics and linear programming, in: Hybrid Metaheuristics HM'04, at workshop European Conference on Artificial Intelligence ECAI'04, Valencia Spain, August 2004, pp. 101-114.
86. Valouxis C., and Housos E. Hybrid optimization techniques for the workshift and rest assignment of nursing personnel. Artificial Intelligence in Medicine 20 2 (2000) 55-175
87. M. Vasquez, J.-K. Hao, A hybrid approach for the 0/1 multidimensional knapsack problem, in: IJCAI, 2001, pp. 328-333.