Swarm intelligence

Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques

Volumn , Issue , 2005, Pages 401-435

  Merkle, Daniel ^a   Middendorf, Martin ^a  

^a UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84892068147     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/0-387-28356-0_14     Document Type: Chapter

References (83)

1. Agassounoun W., Martinoli, A. and Goodman, R., 2001, A scalable, distributed algorithm for allocating workers in embedded systems, in: Proc. 2001 IEEE Systems, Man and Cybernetics Conf., pp. 3367-3373.
2. Amin, K. A., Mikler, A. R. and Iyengar, P. V., Dynamic agent population in agent-based distance vector routing, J. Neural Parallel Sci. Comput. 11:127-142.
3. Bauer, A., Bullnheimer, B., Hartl, R. F. and Strauss, C, 1999, An ant colony optimization approach for the single machine total tardiness problem, in: Proc. CEC'99, IEEE, Piscataway, NJ, pp. 1445-1450.
4. Bautista J. and Pereira, J., 2002, Ant algorithms for assembly line balancing, in: Proc. 3rd Int. Workshop on Ant Algorithms, M. Dorigo et al., eds, Lecture Notes in Computer Science, Vol. 2463, Springer, Berlin, pp. 65-75.
5. Van Den Bergh, F., 2002, An analysis of particle swarm optimizers, Ph. D. Thesis, Department of Computer Science, University of Pretoria, South Africa.
6. Van Den Bergh, F. and Engelbrecht, A. P., 2000, Cooperative learning in neural networks using particle swarm optimizers, S. African Comput. J. 26:84-90.
7. Blackwell, T. M. and Bentley, P. J., 2002, Dynamic search with charged swarms, Proc. Genetic and Evolutionary Computation Conf. 2002 (GECCO 2002), Morgan Kaufmann, San Mateo, CA, pp. 19-26.
8. Blum C. and Sampels, M., 2002a, Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations, in: Proc. 2002 Congress on Evolutionary Computation (CEC'02), pp. 1558-1563.
9. Blum C. and Sampels, M., 2002b, When model bias is stronger than selection pressure. Proc. 7th Int. Conf. on Parallel Problem Solving from Nature (PPSN VII), Lecture Notes in Computer Science, Vol. 2439, Springer, Berlin, pp. 893-902.
10. Bonabeau, E., Dorigo, M. and Theraulaz, G., 1999, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Oxford.
11. Brits, R., Engelbrecht, A. P. and Van Den Bergh, F., 2002, A niching particle swarm optimizer, in: Proc. 4th Asia-Pacific Conf. on Simulated Evolution and Learning (SEAL 2002), pp. 692-696.
12. Bullnheimer, B., Hartl, R. F. and Strau, C. A., 1999, New rank based version of the Ant System: a computational study, Central Eur. J. Open Res. Econ. 7:25-38.
13. Camazine, S., Franks, N. R. and Deneubourg, J.-L., 2001, Self-Organization in Biological Systems, Princeton Studies in Complexity, Princeton University Press, Princeton, NJ.
14. Clerc, M., 1999, The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, in: Proc. Congress of Evolutionary Computation, IEEE, Piscataway, NJ, pp. 1951-1957.
15. Clerc, M., 2002, Think locally, act locally-a framework for adaptive particle swarm optimizers, IEEE J. Evol. Comput., submitted.
16. Clerc, M. and Kennedy, J., 2002, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput. 6:58-73.
17. Conradie, A., Miikkulainen, R., and Aldrich, C, 2002, Adaptive control utilizing neural swarming, Proc. Genetic and Evolutionary Computation Conf. (GECCO 2002), Morgan Kaufmann, San Mateo, CA, pp. 60-67.
18. Cordon, O., Fernandez, I., Herrera, F. and Moreno, L., 2000, A new ACO model integrating evolutionary computation concepts: the best-worst ant system, in: Proc. 2nd Int. Workshop on Ant Algorithms, pp. 22-29.
19. Cordon, O., Herrera, F. and Stützle, T., 2002, A review on the ant colony optimization metaheuristic: basis, models and new trends, Mathware Soft Cornput. 9:141-17'5.
20. Deneubourg, J.-L., Aron, S., Goss, S. and Pasteels, J. M., 1990, The self-organizing exploratory pattern of the Argentine ant, J. Insect Behav. 32:159-168.
21. Dorigo, M., 1992, Optimization, learning and natural algorithms (in Italian), Ph. D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy.
22. Dorigo, M., and Di Caro, G., 1999, The ant colony optimization meta-heuristic, in: New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover, eds, McGraw-Hill, New York, pp. 11-32.
23. Dorigo, M., Di Caro, G. and Sampels, M., eds, 2002a, Proc. 3rd Int. Workshop on Ant Algorithms (ANTS 2002), Lecture Notes in Computer Science, Vol. 2463, Springer, Berlin.
24. Dorigo, M. and Gambardella, L. M., 1997, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1:53-66.
25. Dorigo, M., Maniezzo, V. and Colorni, A., 1991, Positive feedback as a search strategy, Technical Report 91-016, Politecnico di Milano, Italy.
26. Dorigo, M., Maniezzo, V., and Colorni, A., 1996, The ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst., Man Cybernet. B 26:29-41.
27. Dorigo, M., Zlochin, M., Meuleau, N. and Birattari, M., 2002b, Updating ACO pheromones using stochastic gradient ascent and cross-entropy methods, in: Proc. EvoWorkshops 2002, S. Cagnoni et al., eds, Lecture Notes in Computer Science, Vol. 2279, Springer, Berlin, pp. 21-30.
28. Eberhart, R. C, Kennedy, J. and Shi, Y., eds, 2003, Proc. Swarm Intelligence Symp. (Indianapolis, IN).
29. Gambardella, L. M., Taillard, E. and Dorigo, M., 1999, Ant colonies for the quadratic assignment problem, J. Oper. Res. Soc. 50:167-76.
30. Goldberg, D. and Mataric, M. J., 2000, Robust behavior-based control for distributed multi-robot collection tasks, USC Institute for Robotics and Intelligent Systems Technical Report IRIS-00-387.
31. Goss, S., Aron, S., Deneubourg, J. L. and Pasteels, J. M., 1989, Self-organized shortcuts in the Argentine ant, Naturwissenschaften 76:579-581.
32. Guntsch M. and Middendorf, M., 2002a, Applying population based ACO to dynamic optimization problems, in: Proc. 3rd Int. Workshop (ANTS 2002), Lecture Notes in Computer Science, Vol. 2463, Springer, Berlin, pp. 111-122.
33. Guntsch M, and Middendorf, M., 2002b, A population based approach for ACO, in: Applications of Evolutionary Computing-Proc. EvoWorkshops 2002, Lecture Notes in Computer Science, Vol. 2279, Springer, Berlin, pp. 72-81.
34. Gutjahr, W., 2000, A graph-based Ant System and its convergence, Future Generation Comput. Syst. 16:873-888.
35. Gutjahr, W., 2002, ACO algorithms with guaranteed convergence to the optimal solution. Inform. Process. Lett. 82:145-153.
36. Handl, J. and Meyer, B., 2002, Improved ant-based clustering and sorting in a document retrieval interface, in: Proc. 7th Int. Conf. Parallel Problem Solving from Nature-PPSN VII, J. J. Merelo Guervos et al., eds, Lecture Notes in Computer Science, Vol. 2439, Springer, Berlin, pp. 913-923.
37. Hu, X., Eberhart, R. and Shi, Y., 2003, Swarm intelligence for permutation optimization: a case study on n-queens problem, in: Proc. IEEE Swarm Intelligence Symp. 2003 (Indianapolis, IN).
38. Janson, S. and Middendorf, M., 2003, A hierarchical particle swarm optimizer, in: Proc. Congress on Evolutionary Computation (CEC 2003), IEEE, Piscataway, NJ, pp. 770-776.
39. Kawamura, H., Yamamoto, M., Suzuki, K., and Ohucke, A., 2000, Multiple ant colonies algorithm based on colony level interactions, IEICE Trans. Fundamentals A 83:371-379.
40. Kennedy, J. 2000. Stereotyping: improving particle swarm performance with cluster analysis, in: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 1507-1512.
41. Kennedy J. and Eberhart, R. C, 1995, Particle swarm optimization, in: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942-1948.
42. Kennedy, J. and Eberhart, R. C, 1997, A discrete binary version of the particle swarm algorithm. Proc. 1997 Conf on Systems, Man, and Cybernetics, Piscataway, NJ: IEEE Service Center, pp. 4104-4109.
43. Kennedy, J. and Eberhart, R. C, 1999, The particle swarm: social adaption in information processing systems, in: D. Corne et al., eds, New Ideas in Optimization, McGraw-Hill, New York, pp. 379-387.
44. Kennedy, J., Eberhart, R. C, and Shi, Y, 2001, Swarm Intelligence, Morgan Kaufmann, San Francisco, CA.
45. Kennedy, J. and Mendes, R., 2003, Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms, in: Proc. IEEE Int. Workshop on Soft Computing in Industrial Applications.
46. Ko, P.-C. and Lin, P.-C, 2004, A hybrid swarm intelligence based mechanism for earning forecast, in: Proc. 2nd Int. Conf. on Information Technology and Applications (ICITA 2004).
47. Krink, T., Vesterstrøm, J. S. and Riget, J., 2002, Particle swarm optimisation with spatial particle extension, in: Proc. 4th Congress on Evolutionary Computation (CEC-2002), pp. 1474-1479.
48. Kube, C. R. and Bonabeau, E., 2000, Cooperative transport by ants and robots, Robot. Auton. Syst. 30:85-101.
49. Lambrinos, D., Moller, R., Labhart, T., Pfeifer, R., and Wehner, R., 2000, A mobile robot employing insect strategies for navigation, Robot. Auton. Syst. 30:39-64.
50. Lumer, E. D. and Faieta, B., 1994, Diversity and adaptation in populations of clustering ants, in: Proc. 3rd Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats 3 (SAB 94), MIT Press, Cambridge, MA, pp. 501-508.
51. Maniezzo, V., 1999, Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem, INFORMS J. Comput. 11:358-369.
52. Maniezzo, V., and Carbonaro, A., 2001, Ant colony optimization: an overview, in: Essays and Surveys in Metaheuristics, C. Ribeiro, ed., Kluwer, Dordrecht, pp. 21-44.
53. Merkle, D. and Middendorf, M., 2001a, A new approach to solve permutation scheduling problems with ant colony optimization, in: Applications of Evolutionary Computing: Proc. EvoWorkshops 2001, Lecture Notes in Computer Science, Vol. 2037, E. J. W. Boers et al., eds, Springer, Berlin, pp. 484-493.
54. Merkle, D. and Middendorf, M., 2001b, On solving permutation scheduling problems with ant colony optimization, Int. J. Syst. ScL, submitted.
55. Merkle, D. and Middendorf, M., 2002a, Modelling the dynamics of ant colony optimization algorithms, Evol. Comput. 10:235-262.
56. Merkle D. and Middendorf, M., 2002b, Ant colony optimization with the relative pheromone evaluation method, in: Applications of Evolutionary Computing: Proc. EvoWorkshops 2001, Lecture Notes in Computer Science, Vol. 2279, Springer, Berlin, pp. 325-333.
57. Merkle, D., and Middendorf, M., 2003a, On the behavior of ACO algorithms: studies on simple problems, in: Metaheuristics: Computer Decision-Making, M. G. C. Resende and J. Pinho de Sousa, eds, Kluwer, Dordrecht, pp. 465-480.
58. Merkle, D., and Middendorf, M., 2003b, An ant algorithm with global pheromone evaluation for scheduling a single machine, Appl. Intell. 18:105-111.
59. Merkle, D., Middendorf, M. and Schmeck, H., 2002, Ant colony optimization for resource-constrained project scheduling, IEEE Trans. Evol. Comput. 6:333-346.
60. Michels, R. and Middendorf, M., 1999, An ant system for the shortest common supersequence problem, in: New Ideas in Optimization, D. Corne, M. Dorigo and F. Glover, eds, McGraw-Hill, New York, pp. 51-61.
61. Middendorf, M., 2002. Ant colony optimization, in: Tutorial Proc. Genetic and Evolutionary Computation Conf. (GECCO-2002).
62. Montgomery, J., and Randall, M., 2002, Anti-pheromone as a tool for better exploration of search space, in: Proc. 3rd Int. Workshop ANTS 2002, Lecture Notes in Computer Science, Vol. 2463, Springer, Berlin, pp. 100-110.
63. Parsopoulos, K. E. and Vrahatis, M. N., 2001, Modification of the particle swarm optimizer for locating all the global minima, in: Artificial Neural Networks and Genetic Algorithms, V. Kurkova et al., eds, Springer, Berlin, pp. 324-327.
64. Parsopoulos, K. E. and Vrahatis, M. N., 2002, Recent approaches to global optimization problems through particle swarm optimization, Nat. Comput. 1:235-306.
65. Parsopoulos, K. E., Tasoulis, D. K. and Vrahatis, M. N., 2004, Multiobjective optimization using parallel vector evaluated particle swarm optimization, in: Proc. IASTED Int. Conf. on Artificial Intelligence and Applications.
66. Randall, M. and Tonkes, E., 2002, Intensification and diversification strategies in ant colony optimisation, Complexity Int. 9: randal01.
67. Riget, J. and Vesterstrøm, J. S., 2002, A diversity-guided particle swarm optimizer-the ARPSO, Technical Report 2002-02, Department of Computer Science, University of Aarhus.
68. Schoonderwoerd, R., Holland, O., Bruten, J. and Rothkrantz, L., 1996, Ant-based load balancing in telecommunications networks, Adapt. Behav. 5:169-207.
69. Stiitzle, T., Den Besten, M. and Dorigo, M., 2000, Ant colony optimization for the total weighted tardiness problem, in: Proc. 6th Int. Conf. on Parallel Problem Solving from Nature (PPSN-VI), Lecture Notes in Computer Science, Vol. 1917, Deb et al., eds, Springer, Berlin, pp. 611-620.
70. Stiitzle, T. and Dorigo, M., 1999, ACO algorithms for the traveling salesman problem, in: Evolutionary Algorithms in Engineering and Computer Science, K. Miettinen et al., eds, Wiley, New York, pp. 163-183.
71. Stiitzle, T. and Dorigo, M., 2001, An experimental study of the simple ant colony optimization algorithm, in: Proc. 2001 WSES Int. Conf. on Evolutionary Computation (EC'01).
72. Stiitzle, T. and Dorigo, M., 2002a, A short convergence proof for a class of ACO algorithms. IEEE Trans. Evol. Comput. 6:358-365.
73. Stiitzle, T. and Dorigo, M., 2002b, The ant colony optimization metaheuristic: algorithms, applications, and advances, in: Handbook of Metaheuristics, F. Glover and G. Kochenberger, eds, Kluwer, Dordrecht.
74. Stutzle, T. and Hoos, H., 1997, Improvements on the ant system: Introducing MAX (MIN) ant system, in: Proc. Int. Conf. on Artificial Neutral Networks and Genetic Algorithms, Springer, Berlin, pp. 245-249.
75. Stutzle, T., and Hoos, H., 2000, MAX-MIN ant system, Future Gener. Comput. Syst. J. 16:889-914.
76. Suganthan, P. N., 1999, Particle swarm optimizer with neighborhood optimizer, in: Proc. Congress on Evolutionary Computation (CEC 1999), pp. 1958-1961.
77. Sousa, T., Neves, A., and Silva, A., 2003, Swarm optimization as a new tool for data mining, in: Proc. Workshop on Nature Inspired Distributed Computing (NIDISC)-at IPDPS'03, p. 144.
78. Veeramachaneni, K., and Osadciw, L., 2003, Adaptive multimodal biometric fusion algorithm using particle swarm, in: Proc. SPIE Aerosense 2003 (Orlando, FL), pp. 211-222.
79. Vesterstrøm, J. S., Riget, J. and Krink, T., 2002, Division of labor in particle swarm optimisation, in: Proc. 4th IEEE Congress on Evolutionary Computation (CEC-2002), pp. 1570-1575.
80. Xiao, X., Dow, E. R., Eberhart, R., Ben Miled, Z. and Oppelt, R. J., 2003, Gene clustering using self-organizing maps and particle swarm optimization, in: Online Proc. 2nd IEEE Int. Workshop on High Performance Computational Biology (HICOMB 2003).
81. Xie, X.-F., Zhang, W.-J. and Yang, Z.-L., 2002, A dissipative particle swarm optimization, in: Proc. 4th IEEE Congress on Evolutionary Computation (CEC-2002) (Honolulu, HI).
82. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., and Nakanishi, Y, 2000, A particle swarm optimization for reactive power and voltage control considering voltage security assessment, IEEE Trans. Power Syst. 15:1232-1239.
83. OR-Library, 2004, http://mscmga.ms.ic.ac.uk/jeb/orlib/wtinfo.html