Reactive Search Optimization: Learning While Optimizing

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

Abstract

Reactive Search Optimization advocates the integration of sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search Optimization include machine learning and statistics, in particular reinforcement learning, active or query learning, neural networks, and meta-heuristics (although the boundary signalled by the “meta” prefix is not always clear).

Keywords

Transportation Defend Prefix Dispatch Vanilla 

References

  1. 1.
    Abramson, D., Dang, H., Krisnamoorthy, M.: Simulated annealing cooling schedules for the school timetabling problem. Asia-Pac. J. Oper. Res. 16, 1–22 (1999). URL citeseer.ist.psu.edu/article/abramson97simulated.htmlGoogle Scholar
  2. 2.
    Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison-Wesley (1983)Google Scholar
  3. 3.
    Anzellotti, G., Battiti, R., Lazzizzera, I., Lee, P., Sartori, A., Soncini, G., Tecchiolli, G., Zorat, A.: Totem: a highly parallel chip for triggering applications with inductive learning based on the reactive tabu search. In: AIHENP95. Pisa, Italy (1995)Google Scholar
  4. 4.
    Anzellotti, G., Battiti, R., Lazzizzera, I., Soncini, G., Zorat, A., Sartori, A., Tecchiolli, G., Lee, P.: Totem: a highly parallel chip for triggering applications with inductive learning based on the reactive tabu search. Int. J. Mod. Phys. C 6(4), 555–560 (1995)CrossRefGoogle Scholar
  5. 5.
    Arntzen, H., Hvattum, L.M., Lokketangen, A.: Adaptive memory search for multidemand multidimensional knapsack problems. Comput. Oper. Res. 33(9), 2508–2525 (2006). DOI http://dx.doi.org/10.1016/j.cor.2005.07.007 CrossRefGoogle Scholar
  6. 6.
    Avogadro, M., Bera, M., Danese, G., Leporati, F., Spelgatti, A.: The Totem neurochip: an FPGA implementation. In: Signal Processing and Information Technology, 2004. Proceedings of the Fourth IEEE International Symposium on, pp. 461–464 (2004)Google Scholar
  7. 7.
    Balicki, J.: Hierarchical Tabu Programming for Finding the Underwater Vehicle Trajectory. IJCSNS 7(11), 32 (2007)Google Scholar
  8. 8.
    Baluja, S., Barto, A., Boyan, K.B.J., Buntine, W., Carson, T., Caruana, R., Cook, D., Davies, S., Dean, T., et al.: Statistical Machine Learning for Large-Scale Optimization. Neural Comput. Surv. 3, 1–58 (2000)Google Scholar
  9. 9.
    Barnes, J., Wiley, V., Moore, J., Ryer, D.: Solving the aerial fleet refueling problem using group theoretic tabu search. Math. Comput. Model. 39, 617–640 (2004)CrossRefGoogle Scholar
  10. 10.
    Battiti, R., Bertossi, A., Cappelletti, A.: Multilevel Reactive Tabu Search for Graph Partitioning. Preprint UTM 554 (1999)Google Scholar
  11. 11.
    Battiti, R., Bertossi, A.A.: Greedy, prohibition, and reactive heuristics for graph partitioning. IEEE Trans. Comput. 48(4), 361–385 (1999)CrossRefGoogle Scholar
  12. 12.
    Battiti, R., Brunato, M.: Reactive search for traffic grooming in WDM networks. In: S. Palazzo (ed.) Evolutionary Trends of the Internet, IWDC2001, Taormina, Lecture Notes in Computer Science LNCS 2170, pp. 56–66. Springer, Berlin/Heidelberg, Germany (2001)Google Scholar
  13. 13.
    Battiti, R., Brunato, M., Delai, A.: Optimal wireless access point placement for location-dependent services. Technical Report, University di Trento DIT-03-052 (2003)Google Scholar
  14. 14.
    Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization, Operations Research/Computer Science Interfaces, vol. 45. Springer, Berlin/Heidelberg, Germany (2008)Google Scholar
  15. 15.
    Battiti, R., Campigotto, P.: Reinforcement learning and reactive search: an adaptive max-sat solver. In: Ghallab, N.F.M., Spyropoulos, C.D., Avouris N. (eds.) Proceedings ECAI 08: 18th European Conference on Artificial Intelligence, Patras, Greece, 21–25 Jul 2008. IOS Press, Amsterdam (2008)Google Scholar
  16. 16.
    Battiti, R., Lee, P., Sartori, A., Tecchiolli, G.: Combinatorial optimization for neural nets: Rts algorithm and silicon. Technical Report, Dept. of Mathematics, University of Trento, IT (1994). Preprint UTM 435Google Scholar
  17. 17.
    Battiti, R., Lee, P., Sartori, A., Tecchiolli, G.: Totem: A digital processor for neural networks and reactive tabu search. In: Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, MICRONEURO 94, pp. 17–25. IEEE Computer Society Press, Torino, Italy (1994). Preprint UTM 436-June 1994, Università di Trento, ITGoogle Scholar
  18. 18.
    Battiti, R., Lee, P., Sartori, A., Tecchiolli, G.: Special-purpose parallel architectures for high-performance machine learning. In: High Performance Computing and Networking. Milano, Italy (1995). Preprint UTM 445, December 1994, Università di Trento, ITGoogle Scholar
  19. 19.
    Battiti, R., Protasi, M.: Reactive local search for maximum clique. In: Italiano, G.F., Orlando S. (eds.) Proceedings of the Workshop on Algorithm Engineering (WAE’97), Ca’ Dolfin, Venice, Italy, pp. 74–82 (1997)Google Scholar
  20. 20.
    Battiti, R., Protasi, M.: Reactive search, a history-sensitive heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics 2(ARTICLE 2) (1997). http://www.jea.acm.org/
  21. 21.
    Battiti, R., Protasi, M.: Solving MAX-SAT with non-oblivious functions and history-based heuristics. In: Du, D., Gu, J., Pardalos P.M. (eds.) Satisfiability Problem: Theory and Applications, no. 35 in DIMACS: Series in Discrete Mathematics and Theoretical Computer Science, pp. 649–667. American Mathematical Society, Association for Computing Machinery (1997)Google Scholar
  22. 22.
    Battiti, R., Protasi, M.: Reactive local search techniques for the maximum k-conjunctive constraint satisfaction problem (MAX-k-CCSP). Discrete Appl. Math. 96, 3–27 (1999)CrossRefGoogle Scholar
  23. 23.
    Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Algorithmica 29(4), 610–637 (2001)CrossRefGoogle Scholar
  24. 24.
    Battiti, R., Sartori, A., Tecchiolli, G., Tonella, Zorat, A.: Neural compression: an integrated approach to eeg signals. In: Alspector, J., Goodman, R., Brown T.X. (eds.) International Workshop on Applications of Neural Networks to Telecommunications (IWANNT*95), pp. 210–217. Stockholm, Sweden (1995)Google Scholar
  25. 25.
    Battiti, R., Tecchiolli, G.: Learning with first, second, and no derivatives: a case study in high energy physics. Neurocomputing 6, 181–206 (1994)CrossRefGoogle Scholar
  26. 26.
    Battiti, R., Tecchiolli, G.: The reactive tabu search. ORSA J. Comput. 6(2), 126–140 (1994)Google Scholar
  27. 27.
    Battiti, R., Tecchiolli, G.: Simulated annealing and tabu search in the long run: a comparison on QAP tasks. Comput. Math. Appl. 28(6), 1–8 (1994)CrossRefGoogle Scholar
  28. 28.
    Battiti, R., Tecchiolli, G.: Local search with memory: Benchmarking rts. Oper. Res. Spektrum 17(2/3), 67–86 (1995)CrossRefGoogle Scholar
  29. 29.
    Battiti, R., Tecchiolli, G.: Training neural nets with the reactive tabu search. IEEE Trans. Neural Netw. 6(5), 1185–1200 (1995)CrossRefGoogle Scholar
  30. 30.
    Battiti, R., Tecchiolli, G.: The continuous reactive tabu search: blending combinatorial optimization and stochastic search for global optimization. Ann. Oper. Res. – Metaheuristics in Comb. Optimization 63, 153–188 (1996)Google Scholar
  31. 31.
    Baxter, J.: Local optima avoidance in depot location. J. Oper. Res. Soc. 32(9), 815–819 (1981)Google Scholar
  32. 32.
    Bōachut, J.: Tabu search optimization of externally pressurized barrels and domes. Eng. Optimization 39(8), 899–918 (2007)CrossRefGoogle Scholar
  33. 33.
    Boyan, J., Moore, A.: Learning evaluation functions to improve optimization by local search. J. Mach. Learn. Res. 1, 77–112 (2001)Google Scholar
  34. 34.
    Boyan, J.A., Moore, A.W.: Learning evaluation functions for global optimization and boolean satisfability. In: Press A. (ed.) In: Proceedings of 15th National Conf. on Artificial Intelligence (AAAI), pp. 3–10 (1998)Google Scholar
  35. 35.
    Braysy, O.: A reactive variable neighborhood search for the vehicle-routing problem with time windows. INFORMS J. COMPUT. 15(4), 347–368 (2003)CrossRefGoogle Scholar
  36. 36.
    Brunato, M., Battiti, R.: RASH: A self-adaptive random search method. In: Cotta, C., Sevaux, M., Sörensen K. (eds.) Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, vol. 136. Springer, Berlin/Heidelberg, Germany (2008)Google Scholar
  37. 37.
    Brunato, M., Battiti, R., Pasupuleti, S.: A memory-based rash optimizer. In: Geffner, A.F.R.H.H. (ed.) Proceedings of AAAI-06 Workshop on Heuristic Search, Memory Based Heuristics and Their Applications, pp. 45–51. Boston, MA. (2006). ISBN 978-1-57735-290-7Google Scholar
  38. 38.
    Brunato, M., Hoos, H., Battiti, R.: On effectively finding maximal quasi-cliques in graphs. In: Maniezzo, V., Battiti, R., Watson J.P. (eds.) Proceedings 2nd Learning and Intelligent Optimization Workshop, LION 2, Trento, Italy, December 2007, LNCS, vol. 5313. Springer, Berlin/Heidelberg, Germany (2008)Google Scholar
  39. 39.
    Cerulli, R., Fink, A., Gentili, M., Voss, S.: Metaheuristics comparison for the minimum labelling spanning tree problem. The Next Wave on Computing, Optimization, and Decision Technologies, pp. 93–106. Springer, New York (2005)Google Scholar
  40. 40.
    Cerulli, R., Fink, A., Gentili, M., Voß, S.: Extensions of the minimum labelling spanning tree problem. J. Telecommun. Inf. Technol. 4, 39–45 (2006)Google Scholar
  41. 41.
    Chambers, J., Barnes, J.: New tabu search results for the job shop scheduling problem. The University of Texas, Austin, TX, Technical Report Series ORP96-06, Graduate Program in Operations Research and Industrial Engineering (1996)Google Scholar
  42. 42.
    Chambers, J., Barnes, J.: Reactive search for flexible job shop scheduling. Graduate program in Operations Research and Industrial Engineering, The University of Texas at Austin, Technical Report Series, ORP98-04 (1998)Google Scholar
  43. 43.
    Chelouah, R., Siarry, P.: Tabu search applied to global optimization. Eur. J. Oper. Res. 123, 256–270 (2000)CrossRefGoogle Scholar
  44. 44.
    Chiang, W., Russell, R.: A reactive tabu search metaheuristic for the vehicle routing problem with time windows. INFORMS J. Comput. 9, 417–430 (1997)CrossRefGoogle Scholar
  45. 45.
    Codenotti, B., Manzini, G., Margara, L., Resta, G.: Perturbation: An efficient technique for the solution of very large instances of the euclidean tsp. INFORMS J. COMPUT. 8(2), 125–133 (1996)CrossRefGoogle Scholar
  46. 46.
    Connolly, D.: An improved annealing scheme for the QAP. Eur. J. Oper. Res. 46(1), 93–100 (1990)CrossRefGoogle Scholar
  47. 47.
    Consoli, S., Darby-Dowman, K., Geleijnse, G., Korst, J., Pauws, S.: Metaheuristic approaches for the quartet method of hierarchical clustering. Technical Report, Brunel University, West London (2008)Google Scholar
  48. 48.
    Corana, A., Marchesi, M., Martini, C., Ridella, S.: Minimizing multimodal functions of continuous variables with the simulated annealing algorithm. ACM Trans. Math. Softw. 13(3), 262–280 (1987). DOI http://doi.acm.org/10.1145/29380.29864 CrossRefGoogle Scholar
  49. 49.
    Cox, B.J.: Object Oriented Programming, an Evolutionary Approach. Addison-Wesley, Menlo Park, CA (1990)Google Scholar
  50. 50.
    Crispim, J., Brandao, J.: Reactive tabu search and variable neighborhood descent applied to the vehicle routing problem with backhauls. In: Proceedings of the 4th Metaheuristics International Conference, Porto, MIC, pp. 631–636 (2001)Google Scholar
  51. 51.
    Csöndes, T., Kotnyek, B., Zoltán Szabó, J.: Application of heuristic methods for conformance test selection. Eur. J. Oper. Res. 142(1), 203–218 (2002)CrossRefGoogle Scholar
  52. 52.
    Danese, G., De Lotto, I., Leporati, F., Quaglini, A., Ramat, S., Tecchiolli, G.: A parallel neurochip for neural networks implementing the reactive tabu search algorithm: application case studies. In: Parallel and Distributed Processing, 2001. Proceedings. Ninth Euromicro Workshop on, pp. 273–280 (2001)Google Scholar
  53. 53.
    Delmaire, H., Diaz, J., Fernandez, E., Ortega, M.: Reactive GRASP and Tabu Search based heuristics for the single source capacitated plant location problem. INFOR 37, 194–225 (1999)Google Scholar
  54. 54.
    Devarenne, I., Mabed, H., Caminada, A.: Adaptive tabu tenure computation in local search. In: Proceedings 8th European Conference on Evolutionary Computation in Combinatorial Optimisation, Napoli, March 2008, Lecture Notes in Computer Science, vol. 4972, p. 1. Springer, Berlin/Heidelberg, Germany (2008)Google Scholar
  55. 55.
    Eiben, A.E., Horvath, M., Kowalczyk, W., Schut, M.C.: Reinforcement learning for online control of evolutionary algorithms. In: Brueckner, S., Hassas, S., Jelasity, M., Yamins, D. (eds.) Engineering Self-Organising Systems Conference - 4th International Workshop, ESOA 2006, Hakodate, Japan, May 9, 2006. LNAI, vol. 4335. Springer, Berlin/Heidelberg (2006)Google Scholar
  56. 56.
    Faigle, U., Kern, W.: Some convergence results for probabilistic tabu search. ORSA J. Comput. 4(1), 32–37 (1992)Google Scholar
  57. 57.
    Fescioglu-Unver, N., Kokar, M.: Application of Self Controlling Software Approach to Reactive Tabu Search. In: Self-Adaptive and Self-Organizing Systems, 2008. SASO’08. Second IEEE International Conference on, pp. 297–305 (2008)Google Scholar
  58. 58.
    Fink, A., Voß, S.: Applications of modern heuristic search methods to pattern sequencing problems. Comput. Oper. Res. 26(1), 17–34 (1999)CrossRefGoogle Scholar
  59. 59.
    Fink, A., Voß, S.: Solving the continuous flow-shop scheduling problem by metaheuristics. Eur. J. Oper. Res. 151(2), 400–414 (2003)CrossRefGoogle Scholar
  60. 60.
    Fleischer, M.A.: Cybernetic optimization by simulated annealing: Accelerating convergence by parallel processing and probabilistic feedback control. J. Heuristics 1(2), 225–246 (1996)CrossRefGoogle Scholar
  61. 61.
    Fortin, A., Hail, N., Jaumard, B.: A tabu search heuristic for the dimensioning of 3G multi-service networks. Wireless Communications and Networking, 2003. WCNC 2003, vol. 3, pp.1439–1447. IEE Computer Society, Location - Los Alamitos, CA (2003)Google Scholar
  62. 62.
    Fortz, B., , Thorup, M.: Increasing internet capacity using local search. Comput. Optimization. Appl. 29(1), 13–48 (2004)CrossRefGoogle Scholar
  63. 63.
    Frank, J.: Weighting for godot: Learning heuristics for GSAT. In: Proceedings of the National Conference on Artificial Intelligence, vol. 13, pp. 338–343. Wiley, USA (1996)Google Scholar
  64. 64.
    Frank, J.: Learning short-term weights for GSAT. In: Proceedings International Joint Conference on Artificial Intelligence, vol. 15, pp. 384–391. Lawrence Erlbaum, USA (1997)Google Scholar
  65. 65.
    Fukuyama, Y.: Reactive tabu search for distribution load transfer operation. In: Power Engineering Society Winter Meeting, 2000. vol. 2. IEEE Computer Society, Los Alamitos, CA (2000)Google Scholar
  66. 66.
    Genji, T., Oomori, T., Miyazato, K., Hayashi, N., Fukuyama, Y., Co, K.: Service Restoration in Distribution Systems Aiming Higher Utilization Rate of Feeders. In: Proceedings of the Fifth Metaheuristics International Conference (MIC2003), Kyoto, Japan (2003)Google Scholar
  67. 67.
    Gent, I., Walsh, T.: Towards an understanding of hill-climbing procedures for sat. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 28–33. AAAI Press/The MIT Press, Cambridge, MA (1993)Google Scholar
  68. 68.
    Glover, F.: Tabu search–-part i. ORSA J. Comput. 1(3), 190–260 (1989)Google Scholar
  69. 69.
    Glover, F.: Tabu search–-part ii. ORSA J. Comput. 2(1), 4–32 (1990)Google Scholar
  70. 70.
    Hamza, K., Mahmoud, H., Saitou, K.: Design optimization of N-shaped roof trusses using reactive taboo search. Appl. Soft Comput. J. 3(3), 221–235 (2003)CrossRefGoogle Scholar
  71. 71.
    Hamza, K., Saitou, K., Nassef, A.: Design optimization of a vehicle b-pillar subjected to roof crush using mixed reactive taboo search. pp. 1–9. Chicago, Illinois (2003)Google Scholar
  72. 72.
    Hansen, N.M.P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)CrossRefGoogle Scholar
  73. 73.
    Hansen, P., Jaumard, B.: Algorithms for the maximum satisfiability problem. Comput. 44, 279–303 (1990)CrossRefGoogle Scholar
  74. 74.
    Hansen, P., Mladenovic, N.: Variable neighborhood search. In: Burke, E., Kendall G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 211–238. Springer, Berlin/Heidelberg, Germany (2005)Google Scholar
  75. 75.
    Hansmann, U.H.E.: Simulated annealing with tsallis weights a numerical comparison. Physica A: Stat. Theor. Phys. 242(1–2), 250–257 (1997). DOI: 10.1016/S0378-4371(97)00203-3CrossRefGoogle Scholar
  76. 76.
    Hifi, M., Michrafy, M.: A reactive local search-based algorithm for the disjunctively constrained knapsack problem. J. Oper. Res. Soc. 57(6), 718–726 (2006)CrossRefGoogle Scholar
  77. 77.
    Hifi, M., Michrafy, M., Sbihi, A.: A Reactive Local Search-Based Algorithm for the Multiple-Choice Multi-Dimensional Knapsack Problem. Comput. Optimization. Appl. 33(2), 271–285 (2006)CrossRefGoogle Scholar
  78. 78.
    Hu, B., Raidl, G.R.: Variable neighborhood descent with self-adaptive neighborhood-ordering. In: Cotta, C., Fernandez, A.J., Gallardo J.E. (eds.) Proceedings of the 7th EU/MEeting on Adaptive, Self-Adaptive, and Multi-Level Metaheuristics, Malaga, Spain (2006)Google Scholar
  79. 79.
    Hutter, F., Babic, D., Hoos, H.H., Hu, A.J.: Boosting verification by automatic tuning of decision procedures. In: Baumgartner, J., Sheeran, M. (eds.) Proceedings of Formal Methods in Computer Aided Design (FMCAD’07), pp. 27–34. IEEE Computer Society, Los Alamitos, CA (2006)Google Scholar
  80. 80.
    Hutter, F., Hamadi, Y., Hoos, H., Leyton-Brown, K.: Performance prediction and automated tuning of randomized and parametric algorithms. In: Proceedings of the 12th International Conference on Principles and Practice of Constraint Programming (CP 2006). Springer, Berlin/Heidelberg, Germany (2006)Google Scholar
  81. 81.
    Hutter, F., Hoos, H., Stutzle, T.: Automatic algorithm configuration based on local search. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22, p. 1152. Menlo Park, CA; Cambridge, MA 1999, AAAI Press MIT Press London (2007)Google Scholar
  82. 82.
    Ingber, L.: Very fast simulated re-annealing. Math. Comput. Model. 12(8), 967–973 (1989)CrossRefGoogle Scholar
  83. 83.
    Ishtaiwi, A., Thornton, J.R., A. Anbulagan, S., Pham, D.N.: Adaptive clause weight redistribution. In: Proceedings of the 12th International Conference on the Principles and Practice of Constraint Programming, CP-2006, Nantes, France, pp. 229–243 (2006)Google Scholar
  84. 84.
    Kernighan, B., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49, 291–307 (1970)Google Scholar
  85. 85.
    Kincaid, R., Laba, K.: Reactive Tabu Search and sensor selection in active structural acoustic control problems. J. Heuristics 4(3), 199–220 (1998)CrossRefGoogle Scholar
  86. 86.
    Kinney, G., Barnes, J., Colletti, B.: A reactive Tabu Search algorithm with variable clustering for the Unicost Set Covering Problem. Int. J. Oper. Res. 2(2), 156–172 (2007)CrossRefGoogle Scholar
  87. 87.
    Kinney Jr, G., Hill, R., Moore, J.: Devising a quick-running heuristic for an unmanned aerial vehicle (UAV) routing system. J. Oper. Res. Soc. 56, 776–786 (2005)CrossRefGoogle Scholar
  88. 88.
    Kirkpatrick, S., Jr., C.D.G., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)Google Scholar
  89. 89.
    Laarhoven, P.J.M., Aarts, E.H.L. (eds.): Simulated annealing: theory and applications. Kluwer, Norwell, MA, USA (1987)Google Scholar
  90. 90.
    Lenne, R., Solnon, C., Stutzle, T., Tannier, E., Birattari, M.: Reactive stochastic local search algorithms for the genomic median problem. Lecture Notes in Computer Science 4972, 266. Springer, Berlin/Heidelberg (2008)Google Scholar
  91. 91.
    Login, A., Areas, S.: Reactive tabu adaptive memory programming search for the vehicle routing problem with backhauls. J. Oper. Res. Soc. 58, 1630–1641 (2007)CrossRefGoogle Scholar
  92. 92.
    Lourenco, H.: Job-shop scheduling: computational study of local search and large-step optimization methods. Euro. J. Oper. Res. 83, 347–364 (1995)CrossRefGoogle Scholar
  93. 93.
    Magdon-Ismail, M., Goldberg, M., Wallace, W., Siebecker, D.: Locating hidden groups in communication networks using hidden markov models. Lecture Notes in Computer Science, vol. 2665, pp. 126–137. Springer, Berlin/Heidelberg (2003)Google Scholar
  94. 94.
    Martin, O., Otto, S.W., Felten, E.W.: Large-step Markov chains for the traveling salesman problem. Complex Syst. 5:3, 299 (1991)Google Scholar
  95. 95.
    Martin, O., Otto, S.W., Felten, E.W.: Large-step Markov chains for the tsp incorporating local search heuristics. Oper. Res. Lett. 11, 219–224 (1992)CrossRefGoogle Scholar
  96. 96.
    Martin, O.C., Otto, S.W.: Combining simulated annealing with local search heuristics. Ann. of Oper. Res. 63, 57–76 (1996)CrossRefGoogle Scholar
  97. 97.
    Mastrolilli, M., Gambardella, L.: MAX-2-SAT: How good is tabu search in the worst-case? In: Proceedings of the National Conference on Artificial Intelligence, pp. 173–178. Menlo Park, CA; Cambridge, MA 1999. AAAI Press MIT Press, London (2004)Google Scholar
  98. 98.
    Morris, P.: The breakout method for escaping from local minima. In: Proceedings of the National Conference on Artificial Intelligence, vol. 11, p. 40. Wiley, USA (1993)Google Scholar
  99. 99.
    Nahar, S., Sahni, S., Shragowitz, E.: Experiments with simulated annealing. In: DAC ’85: Proceedings of the 22nd ACM/IEEE conference on Design automation, pp. 748–752. ACM Press, New York, NY, USA (1985). DOI http://doi.acm.org/10.1145/317825.317977
  100. 100.
    Nahar, S., Sahni, S., Shragowitz, E.: Simulated annealing and combinatorial optimization. In: DAC ’86: Proceedings of the 23rd ACM/IEEE conference on Design automation, pp. 293–299. IEEE Press, Piscataway, NJ, USA (1986)Google Scholar
  101. 101.
    Nanry, W., Wesley Barnes, J.: Solving the pickup and delivery problem with time windows using reactive tabu search. Transportation Res. Part B 34(2), 107–121 (2000)CrossRefGoogle Scholar
  102. 102.
    Nonobe, K., Ibaraki, T.: A tabu search approach for the constraint satisfaction problem as a general problem solver. Euro. J. Oper. Res. (106), 599–623 (1998)Google Scholar
  103. 103.
    Oomori, T., Genji, T., Yura, T., Takayama, S., Watanabe, T., Fukuyama, Y., Center, T., Inc, K., Hyogo, J.: Fast optimal setting for voltage control equipment considering interconnection of distributed generators. In: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, vol. 2 (2002)Google Scholar
  104. 104.
    Osman, I., Wassan, N.: A reactive tabu search meta-heuristic for the vehicle routing problem with back-hauls. J. Scheduling 5(4), 263–285 (2002)CrossRefGoogle Scholar
  105. 105.
    Osman, I.H.: Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. 41(1–4), 421–451 (1993)CrossRefGoogle Scholar
  106. 106.
    Pasupuleti, S., Battiti, R.: The gregarious particle swarm optimizer (G-PSO). In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 67–74. ACM New York, NY, USA (2006)Google Scholar
  107. 107.
    Potocnik, P., Grabec, I.: Adaptive self-tuning neurocontrol. Math. Comput. Simulation 51 (3-4), 201–207 (2000)CrossRefGoogle Scholar
  108. 108.
    Russell, R., Chiang, W., Zepeda, D.: Integrating multi-product production and distribution in newspaper logistics. Comput. Oper. Res. 35(5), 1576–1588 (2008)CrossRefGoogle Scholar
  109. 109.
    Russell, R., Urban, T.: Vehicle routing with soft time windows and Erlang travel times. J. Oper. Res. Soc. (2007)Google Scholar
  110. 110.
    Ryan, J., Bailey, T., Moore, J., Carlton, W.: Reactive tabu search in unmanned aerial reconnaissance simulations. Proceedings of the 30th conference on Winter simulation, pp. 873–880 (1998)Google Scholar
  111. 111.
    Sammoud, O., Sorlin, S., Solnon, C., Ghédira, K.: A comparative study of ant colony optimization and reactive search for graph matching problems. In: Gottlieb, J., Raidl G.R. (eds.) Evolutionary Computation in Combinatorial Optimization – EvoCOP 2006, LNCS, vol. 3906, pp. 230–242. Springer, Budapest (2006)Google Scholar
  112. 112.
    Schuurmans, D., Southey, F., Holte, R.: The exponentiated subgradient algorithm for heuristic boolean programming. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 17, pp. 334–341. Lawrence Erlbaum, USA (2001)Google Scholar
  113. 113.
    Selman, B., Kautz, H.: Domain-independent extensions to GSAT: solving large structured satisfiability problems. In: Proceedings of IJCAI-93, pp. 290–295 (1993)Google Scholar
  114. 114.
    Selman, B., Kautz, H.: An empirical study of greedy local search for satisfiability testing. In: Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93). Washington, D.C. (1993)Google Scholar
  115. 115.
    Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proceedings of the National Conference on Artificial Intelligence, vol. 12. Wiley, USA (1994)Google Scholar
  116. 116.
    Selman, B., Kautz, H., Cohen, B.: Local search strategies for satisfiability testing. In: Trick, M., Johson D.S. (eds.) Proceedings of the Second DIMACS Algorithm Implementation Challenge on Cliques, Coloring and Satisfiability, no. 26 in DIMACS Series on Discrete Mathematics and Theoretical Computer Science, pp. 521–531 (1996)Google Scholar
  117. 117.
    Selman, B., Levesque, H., Mitchell, D.: A new method for solving hard satisfiability problems. In: Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 440–446. San Jose, CA (1992)Google Scholar
  118. 118.
    Shmygelska, A.: Novel Heuristic Search Methods for Protein Folding and Identification of Folding Pathways. Ph.D. thesis, The University of British Columbia (2006)Google Scholar
  119. 119.
    Shmygelska, A.: An extremal optimization search method for the protein folding problem: the go-model example. In: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, pp. 2572–2579. ACM Press, New York, NY, USA (2007)Google Scholar
  120. 120.
    Shmygelska, A., Hoos, H.: An adaptive bin framework search method for a beta-sheet protein homopolymer model. BMC Bioinform. 8(1), 136 (2007)CrossRefGoogle Scholar
  121. 121.
    Steiglitz, K., Weiner, P.: Algorithms for computer solution of the traveling salesman problem. In: Proceedings of the Sixth Allerton Conference on Circuit and System Theory, Urbana, Illinois, pp. 814–821. IEEE, New York (1968)Google Scholar
  122. 122.
    Taillard, E.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17, 443–455 (1991)CrossRefGoogle Scholar
  123. 123.
    Tompkins, D., Hoos, H.: Warped landscapes and random acts of SAT solving. Proceedings of the Eighth International Symposium on Artificial Intelligence and Mathematics (ISAIM-04) (2004)Google Scholar
  124. 124.
    Tompkins, F.H.D., Hoos, H.: Scaling and probabilistic smoothing: efficient dynamic local search for sat. In: Proceedings Principles and Practice of Constraint Programming–-CP 2002 : 8th International Conference, CP 2002, Ithaca, NY, USA, September 9–13, LNCS, vol. 2470, pp. 233–248. Springer, Berlin/Heidelberg, Germany (2002)Google Scholar
  125. 125.
    Toune, S., Fudo, H., Genji, T., Fukuyama, Y., Nakanishi, Y.: Comparative study of modern heuristic algorithms to service restoration in distribution systems. IEEE Trans. Power Deliv. 17(1), 173–181 (2002)CrossRefGoogle Scholar
  126. 126.
    Vossen, T., Verhoeven, M., ten Eikelder, H., Aarts, E.: A quantitative analysis of iterated local search. Computing Science Reports 95/06, Department of Computing Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands (1995)Google Scholar
  127. 127.
    Voudouris, C., Tsang, E.: Partial constraint satisfaction problems and guided local search. In: Proceedings of 2nd International Conference on Practical Application of Constraint Technology (PACT 96), London, pp. 337–356 (1996)Google Scholar
  128. 128.
    Voudouris, C., Tsang, E.: Guided local search and its application to the traveling salesman problem. Eur. J. Oper. Res. 113, 469–499 (1999)CrossRefGoogle Scholar
  129. 129.
    Wah, B., Wu, Z.: Penalty formulations and trap-avoidance strategies for solving hard satisfiability problems. J. Comput. Sci. Tech. 20(1), 3–17 (2005)CrossRefGoogle Scholar
  130. 130.
    White, S.: Concepts of scale in simulated annealing. In: AIP Conference Proceedings, vol. 122, pp. 261–270 (1984)Google Scholar
  131. 131.
    Winter, T., Zimmermann, U.: Real-time dispatch of trams in storage yards. Ann. Oper. Res. (96), 287–315 (2000). URL http://citeseer.ist.psu.edu/winter00realtime.html CrossRefGoogle Scholar
  132. 132.
    Youssef, S., Elliman, D.: Reactive prohibition-based ant colony optimization (rpaco): a new parallel architecture for constrained clique sub-graphs. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 63–71. IEEE Computer Society, Washington, DC, USA (2004)Google Scholar
  133. 133.
    Zennaki, M., Ech-cherif, A., Lamirel, J.: Using reactive tabu search in semi-supervised classification. In: Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference, Patras, Greece, vol. 2. IEEE Computer Society, Los Alamitos, CA (2007)Google Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.LION Lab, Università di TrentoTrentoItaly

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