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Memory and Learning in Metaheuristics

  • Arif ArinEmail author
  • Ghaith Rabadi
Chapter
  • 3.6k Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

The rapid increase of dimensions and complexity of real life problems makes it more difficult to find optimal solutions by traditional optimization methods. This challenge requires intelligent and sophisticated algorithms to make the right decisions given a set of inputs and a variety of possible actions. In the problem solving arena, this definition is transformed into the term of artificial intelligence. Artificial intelligence emerges in metaheuristics via memory and learning in algorithms. Metaheuristics are promising approaches that can find near-optimal solutions in an acceptable amount of time. Many successful metaheuristics employ “intelligent” procedures to obtain high quality solutions for discrete optimization problems. To demonstrate the contribution of memory and learning into metaheuristics, Estimation of Distribution Algorithms will be incorporated as a memory and learning mechanism into Meta-RaPS (Meta-heuristic for Randomized Priority Search) which is classified as a memoryless metaheuristic. The 0-1 multidimensional knapsack problem will be used to evaluate the “intelligence” of the new algorithm.

Keywords

Artificial intelligence memory learning metaheuristics Meta-RaPS 0-1 multidimensional knapsack problem 
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References

  1. 1.
    Yang, X.-S.: Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons, Inc., New Jersey (2010)Google Scholar
  2. 2.
    Turing, A.M.: On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2 41, 230–267 (1936)Google Scholar
  3. 3.
    Turing, A.M.: Computing Machinery and Intelligence. Mind 59, 433–460 (1950)MathSciNetGoogle Scholar
  4. 4.
    Mumford, C.L., Jain, L.C.: Computational Intelligence: Collaboration, Fusion and Emergence. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  5. 5.
    Pedrycz, W.: Computational Intelligence: An Introduction. CRC Press (1997)Google Scholar
  6. 6.
    Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. John Wiley and Sons (2007)Google Scholar
  7. 7.
    Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer (2004)Google Scholar
  8. 8.
    Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. SCI, vol. 197. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  9. 9.
    Moraga, R.J.: Meta-RaPS. Optimization Methods Class Notes. Northern Illinois University, IL (2009)Google Scholar
  10. 10.
    Glover, F., Laguna, M.: Tabu Search, University of Colorado, Boulder. Kluwer Academic Publishers, Boston (1997)zbMATHGoogle Scholar
  11. 11.
    Webster‘s New Universal Unbridged Dictionary. Random house Value Publishing, Inc., Barnes & Nobles Books, New York (1996)Google Scholar
  12. 12.
    Kazdin, A.E.: Encyclopedia of Psychology. Oxford University Press, USA (2000)Google Scholar
  13. 13.
    Kesner, R.P.: Neurobiology of Learning and Memory. In: Martinez Jr., J.L., Kesner, R.P. (eds.) Neurobiological Views of Memory. Academic Press, California (1998)Google Scholar
  14. 14.
    Anderson, J.R.: Learning and memory: An integrated approach. John Wiley & Sons, New York (2000)Google Scholar
  15. 15.
    Ormrod, J.E.: Human Learning. Pearson Education, Inc., New Jersey (2008)Google Scholar
  16. 16.
    Chance, P.: Learning and Behavior: Active Learning Edition, Belmont, CA (2008)Google Scholar
  17. 17.
    Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Springer, New York (2008)zbMATHGoogle Scholar
  18. 18.
    Talbi, E.G.: Metaheuristics, From Design to Implementation, University of Lille. John Wiley & Sons, Inc., New Jersey (2009)zbMATHGoogle Scholar
  19. 19.
    Rochat, Y., Taillard, E.: Probabilistic Diversification and Intensification in Local Search for Vehicle Routing. Journal of Heuristics 1(1), 147–167 (1995)zbMATHGoogle Scholar
  20. 20.
    Dréo, J., Aumasson, J.-P., Tfaili, W., Siarry, P.: Adaptive Learning Search, A New Tool To Help Comprehending Metaheuristics. International Journal on Artificial Intelligence Tools 16(3) (2007)Google Scholar
  21. 21.
    Battiti, R., Tecchiolli, G.: The Reactive Tabu Search. ORSA Journal on Computing 6(2), 126–140 (1994)zbMATHGoogle Scholar
  22. 22.
    Glover, F.: Tabu search: Part I. ORSA Journal on Computing 1(3), 190–206 (1989)zbMATHGoogle Scholar
  23. 23.
    Chen, X., Yang, J., Li, Z., Tian, D., Shao, Z.: A combined global and local search method to deal with constrained optimization for continuous tabu search. J. Numer. Meth. Engng. 76, 1869–1891 (2008)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Flisberga, P., Lidéna, B., Rönnqvist, M.: A hybrid method based on linear programming and tabu search for routing of logging trucks. Computers & Operations Research 36, 1122–1144 (2009)Google Scholar
  25. 25.
    Hung, Y.-F., Chen, W.-C.: A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm. J. Glob. Optim. 51, 133–148 (2011)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Thamilselvan, R., Balasubramanie, P.: A Genetic Algorithm with a Tabu Search (GTA) for Traveling Salesman Problem. International Journal of Recent Trends in Engineering 1(1), 607–610 (2009)Google Scholar
  27. 27.
    Yeh, S.-F., Chu, C.-W., Chang, Y.-J., Lin, M.-D.: Applying tabu search and simulated annealing to the optimal design of sewer networks. Engineering Optimization 43(2), 159–174 (2011)Google Scholar
  28. 28.
    Wang, Y., Li, L., Ni, J., Huang, S.: Feature selection using tabu search with long-term memories and probabilistic neural networks. Pattern Recognition Letters 30, 661–670 (2009)Google Scholar
  29. 29.
    Karimi, A., Nobahari, H., Siarry, P.: Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions. Comput. Optim. Appl. 45, 639–661 (2010)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Duarte, A., Martí, R., Glover, F., Gortazar, F.: Hybrid scatter tabu search for unconstrained global optimization. Ann. Oper. Res. 183, 95–123 (2011)MathSciNetzbMATHGoogle Scholar
  31. 31.
    Bilge, Ü., Kurtulan, M., Kırac, F.: A tabu search algorithm for the single machine total weighted tardiness problem. European Journal of Operational Research 176, 1423–1435 (2007)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Pitts Jr., R.A., Ventura, J.A.: Scheduling flexible manufacturing cells using Tabu Search. International Journal of Production Research 47(24), 6907–6928 (2009)zbMATHGoogle Scholar
  33. 33.
    Shiguemoto, A.L., Armentano, V.A.: A tabu search procedure for coordinating production, inventory and distribution routing problems. Intl. Trans. in Op. Res. 17, 179–195 (2010)zbMATHGoogle Scholar
  34. 34.
    Pacheco, J., Casado, S., Núñez, L.: A variable selection method based on Tabu search for logistic regression models. European Journal of Operational Research 199, 506–511 (2009)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Brandão, J.: A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. European Journal of Operational Research 195, 716–728 (2009)zbMATHGoogle Scholar
  36. 36.
    Derigs, U., Reuter, K.: A simple and efficient tabu search heuristic for solving the open vehicle routing problem. Journal of the Operational Research Society 60, 1658–1669 (2009)zbMATHGoogle Scholar
  37. 37.
    Wassan, N.: Reactive Tabu Adaptive Memory Programming Search for the Vehicle Routing Problem with Backhauls. Journal of the Operational Research Society 58, 1630–1641 (2007)Google Scholar
  38. 38.
    Chiang, W., Russell, R.A.: A Reactive Tabu Search Metaheuristic for the Vehicle Routing Problem with Time Windows, University of Tulsa. INFORMS Journal on Computing 9(4), 417–430 (1997)zbMATHGoogle Scholar
  39. 39.
    Glover, F., Laguna, M.: Tabu Search. In: Reeves, C.R. (ed.) Modern Heuristic Techniques for Combinatorial Problems, pp. 70–150. Blackwell Publishing, Oxford (1993)Google Scholar
  40. 40.
    Wassan, N.: A Reactive Tabu Search for the Vehicle Routing Problem. Journal of the Operational Research Society 57, 111–116 (2006)zbMATHGoogle Scholar
  41. 41.
    Wassan, N.A., Wassan, A.H., Nagy, G.: A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries. J. Comb. Optim. 15, 368–386 (2008)MathSciNetzbMATHGoogle Scholar
  42. 42.
    Paraskevopoulos, D.C., Repoussis, P.P., Tarantilis, C.D., Ioannou, G., Prastacos, G.P.: A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. J. Heuristics 14, 425–455 (2008)zbMATHGoogle Scholar
  43. 43.
    Holland, J.H.: Outline for a Logical Theory of Adaptive Systems. Journal of the ACM 3, 297–314 (1962)Google Scholar
  44. 44.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  45. 45.
    Rechenberg, I.: Cybernetic Solution Path of an Experimental Problem. Technical Report. Royal Aircraft Establishment Library Translation No. 1112, Farnborough, UK (1965)Google Scholar
  46. 46.
    Rechenberg, I.: Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog (1973)Google Scholar
  47. 47.
    Schwefel, H-P.: Kybernetische Evolution als Strategie der Experimentellen Forschung in der Strömungstechnik. Technical Report. Diplomarbeit Hermann Fottinger Institut für Strömungstechnik. Technische Universität, Berlin, Germany (1965)Google Scholar
  48. 48.
    Fogel, L.J.: Toward Inductive Inference Automata. In: Proceedings of the International Federation for Information Processing Congress, Munich, pp. 395–399 (1962)Google Scholar
  49. 49.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. Wiley (1966)Google Scholar
  50. 50.
    Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  51. 51.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)Google Scholar
  52. 52.
    Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer (2002)Google Scholar
  53. 53.
    Tsoulos, I.G.: Solving constrained optimization problems using a novel genetic algorithm. Applied Mathematics and Computation 208(1), 273–283 (2009)MathSciNetzbMATHGoogle Scholar
  54. 54.
    Vasanthi, T., Arulmozhi, G.: Optimal allocation problem using genetic algorithm. International Journal of Operational Research 5(2), 211–228 (2009)zbMATHGoogle Scholar
  55. 55.
    YoungSu, Y., Chiung, M., Daeho, K.: Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems. Computers & Industrial Engineering 56(3), 821–838 (2009)Google Scholar
  56. 56.
    Awad, M.M., Chehdi, K.: Satellite image segmentation using hybrid variable genetic algorithm. International Journal of Imaging Systems and Technology 19(3), 199–207 (2009)Google Scholar
  57. 57.
    Maruyama, T., Igarashi, H.: An effective robust optimization based on genetic algorithm. IEEE Transactions on Magnetics 44(6), 990–993 (2008)Google Scholar
  58. 58.
    Liu, J.-L., Chen, C.-M.: Improved intelligent genetic algorithm applied to long-endurance airfoil optimization design. Engineering Optimization 41(2), 137–154 (2009)MathSciNetGoogle Scholar
  59. 59.
    Srivastava, P.R.: Optimisation of software testing using genetic algorithm. International Journal of Artificial Intelligence and Soft Computing 1(2-4), 363–375 (2009)Google Scholar
  60. 60.
    Garcia, J., Perez, O., Berlanga, A., Molina, J.M.: Video tracking system optimization using evolution strategies. International Journal of Imaging Systems and Technology 17(2), 75–90 (2007)Google Scholar
  61. 61.
    Abad, A., Elipe, A.: Evolution strategies for computing periodic orbits. Advances in the Astronautical Sciences 134, 673–684 (2009)Google Scholar
  62. 62.
    Mester, D., Braysy, O.: Active-guided evolution strategies for large-scale capacitated vehicle routing problems. Computers & Operations Research 34(10), 2964–2975 (2007)zbMATHGoogle Scholar
  63. 63.
    Chang, Y.-H., Wu, T.-T.: Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems. Applied Mathematics and Computation 218(8), 4075–4089 (2011)MathSciNetzbMATHGoogle Scholar
  64. 64.
    Li, R., Eggermont, J., Shir, O.M., Emmerich, M.T.M., Bäck, T., Dijkstra, J., Reiber, J.H.C.: Mixed-Integer Evolution Strategies with Dynamic Niching. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 246–255. Springer, Heidelberg (2008)Google Scholar
  65. 65.
    Deng-Neng, C., Ting-Peng, L.: Knowledge evolution strategies and organizational performance: A strategic fit analysis. Electronic Commerce Research and Applications 10(1), 75–84 (2011)Google Scholar
  66. 66.
    Bäck, T.: Evolution strategies: Basic introduction. In: Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 875–897 (2011)Google Scholar
  67. 67.
    Dong, H., Dong, Y., Zhou, C., Yin, G., Hou, W.: A fuzzy clustering algorithm based on evolutionary programming. Expert Systems with Applications 36(9), 11792–11800 (2009)Google Scholar
  68. 68.
    Tan, S.C., Lim, C.P.: Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification. Journal of Intelligent and Fuzzy Systems 22(2-3), 57–68 (2011)MathSciNetGoogle Scholar
  69. 69.
    Lin, Y.-C., Lin, Y.-C., Su, K.-L.: Production planning based on evolutionary mixed-integer nonlinear programming. ICIC Express Letters 4(5B), 1881–1886 (2010)Google Scholar
  70. 70.
    Huaxiang, Z., Jing, L.: Adaptive evolutionary programming based on reinforcement learning. Information Sciences 178(4), 971–984 (2008)MathSciNetzbMATHGoogle Scholar
  71. 71.
    Liu, Y.: New discoveries in fast evolutionary programming. International Journal of Innovative Computing, Information and Control 7(5B), 2881–2896 (2011)Google Scholar
  72. 72.
    Sun, K.-T., Lin, Y.-C., Wu, C.-Y., Huang, Y.-M.: An application of the genetic programming technique to strategy development. Expert Systems with Applications 36(3), pt. 1, 5157–5161 (2009)Google Scholar
  73. 73.
    Costa, E.O., Pozo, A.T.R., Vergilio, S.R.: A genetic programming approach for software reliability modeling. IEEE Transactions on Reliability 59(1), 222–230 (2010)Google Scholar
  74. 74.
    Li, X.Y., Shao, X.Y., Gao, L.: Optimization of flexible process planning by genetic programming. International Journal of Advanced Manufacturing Technology 38(1-2), 143–153 (2008)zbMATHGoogle Scholar
  75. 75.
    Zhang, Y., Rockett, P.: Application of multiobjective genetic programming to the design of robot failure recognition systems. IEEE Transactions on Automation Science and Engineering 6(2), 372–376 (2009)Google Scholar
  76. 76.
    Oltean, M., Grosan, C., Diosan, L., Mihaila, C.: Genetic programming with linear representation: A survey. International Journal on Artificial Intelligence Tools 18(2), 197–238 (2009)Google Scholar
  77. 77.
    McKay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., O’neill, M.: Grammar-based Genetic programming: A survey. Genetic Programming and Evolvable Machines 11(3-4), 365–396 (2010)Google Scholar
  78. 78.
    Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 40(2), 121–144 (2010)Google Scholar
  79. 79.
    O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in Genetic Programming. Genetic Programming and Evolvable Machines 11(3-4), 339–363 (2010)Google Scholar
  80. 80.
    Glover, F.: Heuristics for Integer Programming Using Surrogate Constraints. Decision Sciences 8, 156–166 (1977)Google Scholar
  81. 81.
    Glover, F., Laguna, M., Marti, R.: Scatter Search and Path Linking. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. Kluwer Academic Publishers (2003)Google Scholar
  82. 82.
    Egea, J.A., Balsa-Canto, E., García, M.-S.G., Banga, J.R.: Dynamic optimization of nonlinear processes with an enhanced scatter search method. Industrial and Engineering Chemistry Research 48(9), 4388–4401 (2009)Google Scholar
  83. 83.
    Caballero, R., Laguna, M., Martí, R., Molina, J.: Scatter tabu search for multiobjective clustering problems. Journal of the Operational Research Society 62(11), 2034–2046 (2011)Google Scholar
  84. 84.
    Baños, R., Gil, C., Reca, J., Martínez, J.: Implementation of scatter search for multi-objective optimization: A comparative study. Computational Optimization and Applications 42(3), 421–441 (2009)MathSciNetzbMATHGoogle Scholar
  85. 85.
    Contreras, I.A., Diaz, J.A.: Scatter search for the single source capacitated facility location problem. Annals of Operations Research 157, 73–89 (2008)MathSciNetzbMATHGoogle Scholar
  86. 86.
    Tang, J., Zhang, J., Pan, Z.: A scatter search algorithm for solving vehicle routing problem with loading cost. Expert Systems with Applications 37(6), 4073–4083 (2010)Google Scholar
  87. 87.
    Saravanan, M., Haq, A.N.: A scatter search algorithm for scheduling optimisation of job shop problems. International Journal of Product Development 10(1-3), 259–272 (2010)Google Scholar
  88. 88.
    Nasiri, M.M., Kianfar, F.: A hybrid scatter search for the partial job shop scheduling problem. International Journal of Advanced Manufacturing Technology 52(9-12), 1031–1038 (2011)Google Scholar
  89. 89.
    Wang, Y.-S., Teng, H.-F., Shi, Y.-J.: Cooperative co-evolutionary scatter search for satellite module layout design. Engineering Computations (Swansea, Wales) 26(7), 761–785 (2009)Google Scholar
  90. 90.
    Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38(10), 13057–13063 (2011)Google Scholar
  91. 91.
    Maenhout, B., Vanhoucke, M.: A hybrid scatter search heuristic for personalized crew rostering in the airline industry. European Journal of Operational Research 206(1), 155–167 (2010)MathSciNetzbMATHGoogle Scholar
  92. 92.
    Liberatore, S., Sechi, G.M.: Location and calibration of valves in water distribution networks using a scatter-search meta-heuristic approach. Water Resources Management 23(8), 1479–1495 (2009)Google Scholar
  93. 93.
    Duarte, A., Martí, R., Gortazar, F.: Path relinking for large-scale global optimization. Soft Computing 15(11), 2257–2273 (2011)Google Scholar
  94. 94.
    Souffriau, W., Vansteenwegen, P., Vanden, B.G., Van Oudheusden, D.: A Path Relinking approach for the Team Orienteering Problem. Computers and Operations Research 37(11), 1853–1859 (2010)MathSciNetzbMATHGoogle Scholar
  95. 95.
    Bozejko, W.: Parallel path relinking method for the single machine total weighted tardiness problem with sequence-dependent setups. Journal of Intelligent Manufacturing 21(6), 777–785 (2010)Google Scholar
  96. 96.
    Nguyen, V.-P., Prins, C., Prodhon, C.: Solving the two-echelon location routing problem by a GRASP reinforced by a learning process and path relinking. European Journal of Operational Research 216(1), 113–126 (2012)MathSciNetzbMATHGoogle Scholar
  97. 97.
    Nascimento, M.C.V., Resende, M.G.C., Toledo, F.M.B.: GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem. European Journal of Operational Research 200(3), 747–754 (2010)zbMATHGoogle Scholar
  98. 98.
    Armentano, V.A., Shiguemoto, A.L., Løkketangen, A.: Source: Tabu search with path relinking for an integrated production-distribution problem. Computers & Operations Research 38(8), 1199–1209 (2011)MathSciNetzbMATHGoogle Scholar
  99. 99.
    Ribeiro, C.C., Vianna, D.S.: A hybrid genetic algorithm for the phylogeny problem using path-relinking as a progressive crossover strategy. International Transactions in Operational Research 16(5), 641–657 (2009)Google Scholar
  100. 100.
    Vallada, E., Ruiz, R.: Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem. Omega 38(1-2), 57–67 (2010)Google Scholar
  101. 101.
    Jaszkiewicz, A., Zielniewicz, P.: Pareto memetic algorithm with path relinking for bi-objective traveling salesperson problem. European Journal of Operational Research 193(3), 885–890 (2009)MathSciNetzbMATHGoogle Scholar
  102. 102.
    Jaeggi, D.M., Parks, G.T., Kipouros, T., Clarkson, P.J.: The development of a multi-objective tabu search algorithm for continuous optimisation problems. European Journal of Operational Research 185(3), 1192–1212 (2008)MathSciNetzbMATHGoogle Scholar
  103. 103.
    Resende, M.G.C., Martí, R., Gallego, M., Duarte, A.: GRASP and path relinking for the max-min diversity problem. Computers & Operations Research 37(3), 498–508 (2010)MathSciNetzbMATHGoogle Scholar
  104. 104.
    Mateus, G.R., Resende, M.G.C., Silva, R.M.A.: GRASP with path-relinking for the generalized quadratic assignment problem. Journal of Heuristics 17(5), 527–565 (2011)zbMATHGoogle Scholar
  105. 105.
    Nascimento, M.C.V., Resende, M.G.C., Toledo, F.M.B.: GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem. European Journal of Operational Research 200(3), 747–754 (2010)zbMATHGoogle Scholar
  106. 106.
    Beni, G.: The Concept of Cellular Robotic System. In: Proceedings 1988 IEEE Int. Symp. on Intelligent Control, Los Alamitos, CA, pp. 57–62 (1988)Google Scholar
  107. 107.
    Beni, G., Wang, J.: Swarm Intelligence. In: Proceedings Seventh Annual Meeting of the Robotics Society of Japan, Tokyo, pp. 425–428 (1989)Google Scholar
  108. 108.
    Hackwood, S., Beni, G.: Self-Organization of Sensors for Swarm Intelligence. In: Proceedings IEEE 1992 International Conference on Robotics and Automation, pp. 819–829. IEEE Computer Society Press, Los Alamitos (1992)Google Scholar
  109. 109.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Sante Fe Institute. Studies in the Sciences of Complexity. Oxford University Press, New York (1999)Google Scholar
  110. 110.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence: Collective, Adaptive. Morgan Kaufmann, San Francisco (2001)Google Scholar
  111. 111.
    Ahuja, R.K., Ergun, O., Orlin, J.B., Punnen, A.P.: A Survey of Very Large Scale Neighborhood Search Techniques. Discrete Applied Mathematics 123, 75–102 (2002)MathSciNetzbMATHGoogle Scholar
  112. 112.
    Nicolis, G., Prigogine, I.: Self-Organization in Non-Equilibrium Systems. Wiley & Sons, New York (1977)Google Scholar
  113. 113.
    Haken, H.: Synergetics. Springer, Berlin (1983)Google Scholar
  114. 114.
    Deneubourg, J.-L., Goss, S., Franks, N.R., Pasteels, J.M.: The Blind Leading the Blind: Modeling Chemically Mediated Army Ant Raid Patterns. J. Insect Behav. 2, 719–725 (1989)Google Scholar
  115. 115.
    Grasse, P.-P.: La Reconstruction du nid et les Coordinations Inter-Individuelles chez Bellicositerm. es Natalensis et Cubitermes sp. La theorie de la Stigmergie: Essai d’interpretation du Comportement des Termites Constructeurs. Insect. Soc. 6, 41–80 (1959)Google Scholar
  116. 116.
    Grasse, P.-P.: Termitologia, Tome II. Fondation des Societes. Construction, Paris, Masson (1984)Google Scholar
  117. 117.
    Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  118. 118.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD thesis. Politecnico di Milano, Italy (1992)Google Scholar
  119. 119.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Massachusetts (2004)zbMATHGoogle Scholar
  120. 120.
    Merkle, D., Middendorf, M.: Swarm Intelligence. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, New York (2005)Google Scholar
  121. 121.
    Chengming, Q.: Ant colony optimization with local search for continuous functions. Advanced Materials Research 204-210, pt. 4, 1135–1138 (2011)Google Scholar
  122. 122.
    Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185(3), 1155–1173 (2008)MathSciNetzbMATHGoogle Scholar
  123. 123.
    Schlüter, M., Egea, J.A., Banga, J.R.: Extended ant colony optimization for non-convex mixed integer nonlinear programming. Computers and Operations Research 36(7), 2217–2229 (2009)MathSciNetzbMATHGoogle Scholar
  124. 124.
    Mei, H., Wang, Y.: Ant colony optimization for neural network. Key Engineering Materials 392-394, 677–681 (2009)Google Scholar
  125. 125.
    Lin, B.M.T., Lu, C.Y., Shyu, S.J., Tsai, C.Y.: Development of new features of ant colony optimization for flowshop scheduling. International Journal of Production Economics 112(2), 742–755 (2008)Google Scholar
  126. 126.
    Mirabi, M.: Ant colony optimization technique for the sequence-dependent flowshop scheduling problem. International Journal of Advanced Manufacturing Technology 55(1-4), 317–326 (2011)Google Scholar
  127. 127.
    Juang, C.-F., Chang, P.-H.: Designing fuzzy-rule-based systems using continuous ant-colony optimization. IEEE Transactions on Fuzzy Systems 18(1), 138–149 (2010)Google Scholar
  128. 128.
    Yeong-Hwa, C., Chia-Wen, C., Chin-Wang, T., Hung-Wei, L., Jin-Shiuh, T.: Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization. Expert Systems with Applications 39(3), 3624–3633 (2012)Google Scholar
  129. 129.
    Yan, C.-Y., Luo, Q.-Q., Chen, Y.: An efficient hybrid evolutionary optimization algorithm combining ant colony optimization with simulated annealing. International Journal of Digital Content Technology and its Applications 5(8), 234–240 (2011)Google Scholar
  130. 130.
    Mavrovouniotis, M., Shengxiang, Y.: A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Computing 15(7), 1405–1425 (2011)Google Scholar
  131. 131.
    Vasko, F.J., Bobeck, J.D., Governale, M.A., Rieksts, D.J., Keffer, J.D.: A statistical analysis of parameter values for the rank-based ant colony optimization algorithm for the traveling salesperson problem. Journal of the Operational Research Society 62(6), 1169–1176 (2011)Google Scholar
  132. 132.
    Ke, L., Feng, Z., Ren, Z., Wei, X.: An ant colony optimization approach for the multidimensional knapsack problem. Journal of Heuristics 16(1), 65–83 (2010)zbMATHGoogle Scholar
  133. 133.
    Yu, B., Yang, Z.-Z., Yao, B.: An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research 196(1), 171–176 (2009)zbMATHGoogle Scholar
  134. 134.
    Silva, C.A., Sousa, J.M.C., Runkler, T.A., Sá da Costa, J.M.G.: Distributed supply chain management using ant colony optimization. European Journal of Operational Research 199(2), 349–358 (2009)MathSciNetzbMATHGoogle Scholar
  135. 135.
    Abdallah, H., Emara, H.M., Dorrah, H.T., Bahgat, A.: Using Ant Colony Optimization algorithm for solving project management problems. Expert Systems with Applications 36(6), 10004–10015 (2009)Google Scholar
  136. 136.
    Deng, G.-F., Lin, W.-T.: Ant colony optimization-based algorithm for airline crew scheduling problem. Expert Systems with Applications 38(5), 5787–5793 (2011)MathSciNetGoogle Scholar
  137. 137.
    Zhang, N., Feng, Z.-R., Ke, L.-J.: Guidance-solution based ant colony optimization for satellite control resource scheduling problem. Applied Intelligence 35(3), 436–444 (2011)Google Scholar
  138. 138.
    Mohan, B.C., Baskaran, R.: A survey: Ant colony optimization based recent research and implementation on several engineering domain. Expert Systems with Applications 39(4), 4618–4627 (2012)Google Scholar
  139. 139.
    Blum, C., Li, X.: Swarm Intelligence in Optimization. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence: Introduction and Applications. Springer, Heidelberg (2008)Google Scholar
  140. 140.
    Zhang, J., Zhang, C., Liang, S.: The circular discrete particle swarm optimization algorithm for flow shop scheduling problem. Expert Systems with Applications 37(8), 5827–5834 (2010)MathSciNetGoogle Scholar
  141. 141.
    Lian, Z.: A united search particle swarm optimization algorithm for multiobjective scheduling problem. Applied Mathematical Modelling 34(11), 3518–3526 (2010)MathSciNetzbMATHGoogle Scholar
  142. 142.
    Leung, S.Y.S., Tang, Y., Wong, W.K.: A hybrid particle swarm optimization and its application in neural networks. Expert Systems with Applications 39(1), 395–405 (2012)Google Scholar
  143. 143.
    Abd-El-Wahed, W.F., Mousa, A.A., El-Shorbagy, M.A.: Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. Journal of Computational and Applied Mathematics 235(5), 1446–1453 (2011)MathSciNetzbMATHGoogle Scholar
  144. 144.
    Bachlaus, M., Pandey, M.K., Mahajan, C., Shankar, R., Tiwari, M.K.: Designing an integrated multi-echelon agile supply chain network: A hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing 19(6), 747–761 (2008)Google Scholar
  145. 145.
    Abido, M.A.: Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research 79(7), 1105–1113 (2009)Google Scholar
  146. 146.
    Elsays, M.A., Aly, M.N., Badawi, A.A.: Optimizing the dynamic response of the H.B. Robinson nuclear plant using multiobjective particle swarm optimization. Kerntechnik 74(1-2), 70–78 (2009)Google Scholar
  147. 147.
    Quan-Ke, P., Tasgetiren, M.F., Yun-Chia, L.: A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Computers and Operations Research 35(9), 2807–2839 (2008)MathSciNetzbMATHGoogle Scholar
  148. 148.
    Guner, A.R., Sevkli, M.: A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem. Journal of Artificial Evolution & Applications, 861512 (9 p.) (2008)Google Scholar
  149. 149.
    Ebrahimi, M., Farmani, M.R., Roshanian, J.: Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization. Structural and Multidisciplinary Optimization 44(6), 773–784 (2011)Google Scholar
  150. 150.
    Pu, H., Zhen, Z., Wang, D., Hu, Y.: Improved particle swarm optimization algorithm for intelligently setting UAV attitude controller parameters. Transactions of Nanjing University of Aeronautics & Astronautics 26(1), 52–57 (2009)Google Scholar
  151. 151.
    Qi-Xin, Z., Fu-Chun, S., Wei, X.: Task allocation for On-orbit servicing spacecrafts using discrete particle Swarm optimization Algorithm. International Journal of Advancements in Computing Technology 3(11), 467–476 (2011)Google Scholar
  152. 152.
    Wu, P., Gao, L., Zou, D., Li, S.: An improved particle swarm optimization algorithm for reliability problems. ISA Transactions 50(1), 71–81 (2011)Google Scholar
  153. 153.
    Ramadan, R.M., Abdel-Kader, R.F.: Face recognition using particle swarm optimization-based selected features. International Journal of Signal Processing, Image Processing and Pattern Recognition 2(2), 51–64 (2008)Google Scholar
  154. 154.
    Kameyama, K.: Particle swarm optimization - a survey. IEICE Transactions on Information and Systems E92-D(7), 1354–1361 (2009)Google Scholar
  155. 155.
    Grahl, J.: Estimation of Distribution Algorithms in Logistics: Analysis, Design, and Application. PhD Thesis. Mannheim University, Dortmund (2007)Google Scholar
  156. 156.
    Baluja, S., Pomerleau, D., Jochem, T.: Towards Automated Artificial Evolution for Computer-Generated Images. Connection Science, 325–354 (1994)Google Scholar
  157. 157.
    Sáez, Y.: Optimization Using Genetic Algorithms with Micropopulations. In: Alba, E., Blum, C., Isasi, P., León, C., Gómez, J.A. (eds.) Optimization Techniques for Solving Complex Problems, John Wiley & Sons Inc, New Jersey (2009)Google Scholar
  158. 158.
    Zhang, Q., Sun, J., Tsang, E., Ford, J.: Estimation of Distribution Algorithm with 2-opt. Local Search for the Quadratic Assignment Problem. In: Lozano, J.A., Larranaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation, Advances in the Estimation of Distribution Algorithms. STUDFUZZ, vol. 192, pp. 281–292. Springer, Heidelberg (2006)Google Scholar
  159. 159.
    Xiao, J., Yan, Y., Zhang, J.: HPBIL: A histogram-based EDA for continuous optimization. Applied Mathematics and Computation 215(3), 973–982 (2009)zbMATHGoogle Scholar
  160. 160.
    Yuan, B., Orlowska, M., Sadiq, S.: Extending a class of continuous estimation of distribution algorithms to dynamic problems. Optimization Letters 2(3), 433–443 (2008)MathSciNetzbMATHGoogle Scholar
  161. 161.
    Qingfu, Z., Aimin, Z., Yaochu, J.: RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12(1), 41–63 (2008)Google Scholar
  162. 162.
    Martí, L., Garca, J., Berlanga, A., Coello Coello, C.A., Molina, J.M.: MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms. Operations Research Letters 39(2), 150–154 (2011)MathSciNetzbMATHGoogle Scholar
  163. 163.
    Hongcheng, L., Liang, G., Quanke, P.: A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Expert Systems with Applications 38(4), 4348–4360 (2011)Google Scholar
  164. 164.
    Huang, X., Jia, P., Liu, B.: Controlling chaos by an improved estimation of distribution algorithm. Mathematical and Computational Applications 15(5 Spec. Issue), 866–871 (2010)MathSciNetzbMATHGoogle Scholar
  165. 165.
    Zhou, Y., Wang, J.: Neural network combined with estimation of distribution for max-cut problem. ICIC Express Letters 4(4), 1161–1166 (2010)Google Scholar
  166. 166.
    Santana, R., Larrañaga, P., Lozano, J.A.: Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. Journal of Heuristics 14(5), 519–547 (2008)zbMATHGoogle Scholar
  167. 167.
    Jarboui, B., Eddaly, M., Siarry, P.: An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems. Computers & Operations Research 36(9), 2638–2646 (2009)MathSciNetzbMATHGoogle Scholar
  168. 168.
    Zhong, X., Ding, J., Li, W., Zhang, Y.: Robust airfoil optimization with multi-objective estimation of distribution algorithm. Chinese Journal of Aeronautics 21(4), 289–295 (2008)Google Scholar
  169. 169.
    Patricio, M.A., García, J., Berlanga, A., Molina, J.M.: Visual data association for real-time video tracking using genetic and estimation of distribution algorithms. International Journal of Imaging Systems and Technology 19(3), 199–207 (2009)Google Scholar
  170. 170.
    Hauschild, M., Pelikan, M.: An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation 1(3), 111–128 (2011)Google Scholar
  171. 171.
    Lozano, J.A., Larrañaga, P., Inz, I., Bengoetxea, E.: Evolutionary Computation: Towards a New Advances in the Estimation of Distribution Algorithms. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  172. 172.
    DePuy, G., Whitehouse, G.E.: A simple and effective heuristic for the multiple resource allocation problem. International Journal of Production Research 32(4), 24–31 (2001)Google Scholar
  173. 173.
    Moraga, R.J.: Meta-RaPS: An Effective Solution Approach for Combinatorial Problems. Ph.D. thesis, University of Central Florida, Orlando, FL (2002)Google Scholar
  174. 174.
    DePuy, G.W., Moraga, R.J., Whitehouse, G.E.: Meta-RaPS: a simple and effective approach for solving the traveling salesman problem. Transportation Research Part E: Logistics and Transportation Review 41(2), 115–130 (2005)Google Scholar
  175. 175.
    Moraga, R.J., DePuy, G.W., Whitehouse, G.E.: Meta-RaPS approach for the 0–1 multidimensional knapsack problem. Computers and Industrial Engineering 48(2), 83–96 (2005)Google Scholar
  176. 176.
    Rabadi, G., Moraga, R., Al-Salem, A.: Heuristics for the unrelated parallel machine scheduling problem with setup times. Journal of Intelligent Manufacturing 17, 85–97 (2006)Google Scholar
  177. 177.
    Hepdogan, S., Moraga, R.J., DePuy, G.W., Whitehouse, G.E.: A Meta-RaPS For The Early/Tardy Single Machine Scheduling Problem. International Journal of Production Research 47(7), 1717–1732 (2009)zbMATHGoogle Scholar
  178. 178.
    Garcia, C., Rabadi, G.: A Meta-RaPS algorithm for spatial scheduling with release times. Int. J. Planning and Scheduling 1(1/2), 19–31 (2011)Google Scholar
  179. 179.
    Kaplan, S., Rabadi, G.: A Simulated Annealing and Meta-RaPS Algorithms for the Aerial Refueling Scheduling Problem with Due Date-to-Deadline Windows and Release Time. Engineering Optimization (in Press)Google Scholar
  180. 180.
    Arcus, A.L.: COMSOAL: A Computer Method of Sequencing Operations for Assembly Lines. The International Journal of Production Research 4(4), 259–277 (1966)Google Scholar
  181. 181.
    Hepdogan, S., Moraga, R.J., DePuy, G.W., Whitehouse, G.E.: A Meta-RaPS for the Early/Tardy Single Machine Scheduling Problem. International Journal of Production Research 47(7), 1717–1732 (2009)zbMATHGoogle Scholar
  182. 182.
    Moraga, R.J., DePuy, G.W., Whitehouse, G.E.: Metaheuristics: A Solution Methodology for Optimization Problems. In: Badiru, A.B. (ed.) Handbook of Industrial and Systems Engineering. CRC Press, FL (2006)Google Scholar
  183. 183.
    Lan, G., DePuy, G.W., Whitehouse, G.E.: An Effective and Simple Heuristic for the Set Covering Problem. European Journal of Operational Research 176, 1387–1403 (2007)MathSciNetzbMATHGoogle Scholar
  184. 184.
    DePuy, G.W., Whitehouse, G.E., Moraga, R.J.: Meta-RaPS: A Simple and Efficient Approach for Solving Combinatorial Problems. In: 29th International Conference on Computers and Industrial Engineering, Montreal, Canada, November 1-3, pp. 644–649 (2001)Google Scholar
  185. 185.
    Gallardo, J.E., Cotta, C., Fernandez, A.J.: Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems, Optimization Techniques for Solving Complex Problems. John Wiley & Sons, Hoboken (2009)Google Scholar
  186. 186.
    Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Chichester (1990)zbMATHGoogle Scholar
  187. 187.
    Freville, A.: The Multidimensional 0–1 Knapsack Problem: An Overview. European Journal of Operational Research 155, 1–21 (2004)MathSciNetzbMATHGoogle Scholar
  188. 188.
    Wilbaut, C., Hanafi, S., Salhi, S.: A Survey of Effective Heuristics and Their Application to a Variety of Knapsack Problems. IMA Journal of Management Mathematics 19, 227–244 (2008)MathSciNetzbMATHGoogle Scholar
  189. 189.
    Battiti, R., Tecchiolli, G.: Local Search with Memory: Benchmarking RTS. OR-Spektrum 17, 67–86 (1995)zbMATHGoogle Scholar
  190. 190.
    Balev, S., Yanev, N., Fréville, A., Andonov, R.: A dynamic programming based reduction procedure for the multidimensional 0–1 knapsack problem. European Journal of Operational Research 186, 63–76 (2008)MathSciNetzbMATHGoogle Scholar
  191. 191.
    Boussier, S., Vasquezb, M., Vimont, Y., Hanafi, S., Michelon, P.: A multi-level search strategy for the 0-1 Multidimensional Knapsack Problem. Discrete Applied Mathematics 158, 97–109 (2010)MathSciNetzbMATHGoogle Scholar
  192. 192.
    Fleszar, K., Hindi, K.S.: Fast, effective heuristics for the 0-1 multi-dimensional knapsack problem. Computers & Operations Research 36, 1602–1607 (2009)MathSciNetzbMATHGoogle Scholar
  193. 193.
    Boyer, V., Elkihel, M., El Baz, D.: Heuristics for the 0–1 multidimensional knapsack problem. European Journal of Operational Research 199, 658–664 (2009)MathSciNetzbMATHGoogle Scholar
  194. 194.
    Wilbaut, C., Hanafi, S.: New convergent heuristics for 0–1 mixed integer programming. European Journal of Operational Research 195, 62–74 (2009)MathSciNetzbMATHGoogle Scholar
  195. 195.
    Fréville, A.: The multidimensional 0-1 knapsack problem - An overview. European Journal of Operational Research 155, 1–21 (2004)MathSciNetzbMATHGoogle Scholar
  196. 196.
    Fréville, A., Hanafi, S.: The multidimensional 0-1 knapsack problem - bounds and computational aspects. Ann. Oper. Res. 139, 195–227 (2005)MathSciNetzbMATHGoogle Scholar
  197. 197.
    Beasley, J.E.: OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Journal Society 41, 170–181 (1990), http://people.brunel.ac.uk/~mastjjb/jeb/info.html Google Scholar
  198. 198.
    Alpaydın, E.: Introduction to Machine Learning. The MIT Press, Cambridge (2004)Google Scholar
  199. 199.
    Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)Google Scholar
  200. 200.
    Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Springer (2003)Google Scholar
  201. 201.
    Panigrahi, B.K., Shi, Y., Lim, M.-H.: Handbook of Swarm Intelligence: Concepts, Principles and Applications. Springer, Heidelberg (2011)zbMATHGoogle Scholar

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Authors and Affiliations

  1. 1.Engineering Management & Systems EngineeringOld Dominion UniversityNorfolkUSA

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