A distributed learning algorithm for particle systems

Integrated Computer-Aided Engineering

Volumn 16, Issue 1, 2009, Pages 1-20

  Rodríguez, Alejandro ^a   Reggia, James A ^a  

^a University of Maryland ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR AUTOMATA; COORDINATION REACTIONS; EDUCATION; GLOBAL OPTIMIZATION; LEARNING SYSTEMS;

CHANGING CONDITIONS; COLLECTIVE BEHAVIORS; COLLECTIVE TRANSPORTS; COOPERATING AGENTS; DISTRIBUTED LEARNINGS; GENERAL PROBLEM SOLVING; LOGISTICS PROBLEMS; PARTICLE SYSTEMS; SCHEDULING PROBLEMS; SELF-ORGANIZING; SELF-ORGANIZING SYSTEMS; SHOP FLOORS; SWARM INTELLIGENCES; SYSTEM DESIGNERS;

LEARNING ALGORITHMS;

EID: 58149520425     PISSN: 10692509     EISSN: None     Source Type: Journal    
DOI: 10.3233/ICA-2009-0301     Document Type: Article

References (40)

1. W. Agassounon, Distributed information retrieval and dissemination in swarm-based networks of mobile, autonomous agents, in: Swarm Intelligence Symposium, 2003 SIS '03 Proceedings of the. 2003 IEEE, 2003, pp. 152-159.
2. P.J. Angeline, Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences, in: EP '98: Proceedings of the 7th International Conference on Evolutionary Programming VII, London, UK, Springer-Verlag, 1998, pp. 601-610.
3. T. Balch and R. Arkin, Behavior-based formation control for multi-robot teams, IEEE Transactions on Robotics and Automation 14(6) (1998), 926-939.
4. J.W. Barnes and J.B. Chambers, Solving the job shop scheduling problem with tabu search. IIE transactions 27(2) (1995), 257-263.
5. G. Beni, The concept of cellular robotic system, in: Proceedings of the IEEE International Symposium on Intelligent Control, 1988, pp. 57-62.
6. E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence, Oxford University Press, 1999.
7. S. Camazine and J. Sneyd, A model of collective nectar source selection by honey bees: Self-organization through simple rules, Journal of Theoretical Biology 149(4) (1991), 547-571.
8. R. Cheng, M. Gen and Y. Tsujimura, A tutorial survey of job-shop scheduling problems using genetic algorithms, Computers and Industrial Engineering 30(4) (1996), 983-997.
9. A. Colorni, M. Dorigo, V. Maniezzo and M. Trubian, Ant system for job-shop scheduling, JORBEL - Belgian Journal of Operations Research, Statistics and Computer Science 34(1) (1994), 39-53.
10. X. Cui, J. Gao and T. Potok, A flocking-based algorithm for document clustering analysis. Journal of Systems Architecture 52 (2006), 505-518.
11. M. Dell' Amico and M. Trubian, Applying tabu search to the job-shop scheduling problem, Annals of Operations Research 41(3) (1993), 231-252.
12. M. Dorigo, G.D. Caro and L. Gambardella, Ant algorithms for discrete optimization, Artificial Life 5(2) (1999), 137-172.
13. M. Dorigo, V. Maniezzo and A. Colorni, The Ant System: Optimization by a colony of cooperating agents, IEEE Trans Systems, Man, and Cybernetics Part B: Cybernetics 26 (1996), 29-41.
14. R. Eberhart and Y. Shi, Particle swarm optimization: Developments, applications and resources, in: Proc 2001 Congress on Evolutionary Comp, Seoul, South Korea, 2001, pp. 81-86.
15. J. Fredslund and M.J. Matarić, A general algorithm for robot formations using local sensing and minimal communication, IEEE Transactions on Robotics and Automation 18(5) (2002), 837-846.
16. M. Garey, D. Johnson and R. Sethi, The complexity of flowshop and jobshop scheduling, Mathematics of Operations Research 1(2) (1976), 117-129.
17. T. Gonzalez and S. Sahni, Flowshop and jobshop schedules: complexity and approximation, Operations Research 26(36) (1978), 52.
18. R. Groß and M. Dorigo, Cooperative transport of objects of different shapes and sizes, in: Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, Vol 3172, M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada and T. Stützle, eds, of Lecture Notes in Computer Science, Springer Verlag, Berlin, Germany, 2004, pp. 107-118.
19. D. Hoitomt, P. Luh and K. Pattipati, A practical approach to job-shop scheduling problems, IEEE Trans Robotics and Automation 9 (1993), 1-13.
20. A. Huth and C. Wiesel, The simulation of the movement of fish schools, Journal of Theoretical Biology 156 (1992), 365-385.
21. L. Kaufman and P. Rousseeuw, Finding groups in data, Wiley Series in Probability and Mathematical Statistics Applied Probability and Statistics, New York: Wiley, 1990.
22. J. Kennedy and R. Eberhart, Particle swarm optimization, in: IEEE Int'l Conf on Neural Networks Vol 4, Piscataway, NJ, 1995, pp. 1942-1948.
23. S. Kirkpatrick, C.D. Gelatt and M. P. Vecchi, Optimization by simulated annealing, Science 220(4598) (1983), 671-680.
24. R.C. Kube and E. Bonabeau, Cooperative transport by ants and robots, Robotics and Autonomous Systems 30(1,2) (2000), 85-101, ISSN: 0921-8890.
25. E. Lawler, J. Lenstra, A.R. Nan and D. Shmoys, Logistics of Production and Inventory, Handbooks in OR & MS 4, chapter Sequencing and scheduling: algorithms and complexity, Elsevier Science, Amsterdam, The Netherlands, 1993, 445-522.
26. M.J. Matarić, Designing and understanding adaptive group behavior, Adaptive Behavior 4(1) (1995), 51-80.
27. Z. Michalewicz and D.B. Fogel, How to Solve It: Modern Heuristics, Springer, 2004.
28. H. Parunak, S. Brueckner and J. Odell, Swarming pattern detection in sensor and robot networks, in: American Nuclear Society (ANS) Topical Meeting on Robotics and Remote Systems, 2004.
29. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves and G.D. Smith, Modern heuristic search methods, John Wiley & Sons, 1996.
30. W.T. Reeves, Particle systems, in: SIGGRAPH '83: Proc 10th Annual Conf on Computer Graphics and Interactive Techniques, 1983, pp. 359-375, ACM Press.
31. C. Reynolds, Flocks, herds, and schools: A distributed behavioral model, Computer Graphics 21(4) (1987), 25-34.
32. A. Rodríguez and J. Reggia, Collective-movement teams for cooperative problem solving, Integrated Computer-aided Engineering 12(3) (2005), 217-235.
33. A. Rodríguez and J.A. Reggia, Extending self-organizing particle systems to problem solving, Artificial Life 10(4) (2004), 379-395.
34. J. Sauter, R. Matthews, H. Parunak and S. Brueckner, Performance of digital pheromones for swarming vehicle control, Proc Fourth Intl Joint Conf Autonomous Agents, Multiagent Systems ACM, (2005), 903-910.
35. C.E. Shannon, A mathematical theory of communication, The Bell System Technical Journal 27 (1948), 379-423, 623-656.
36. D. Subramanian, P. Druschel and J. Chen, Ants and reinforcement learning, Proc IJCAI-97, Intl Joint Conf on Artificial Intelligence Morgan Kaufmann, (832-838), 1997.
37. V. Trianni, S. Nolfi and M. Dorigo, Cooperative hole avoidance in a swarm-bot, Robotics and Autonomous Systems 54(2) (2006), 97-103.
38. X. Tu and D. Terzopoulos, Artificial fishes: Physics, locomotion, perception, behavior, in: Computer Graphics 28 Annual Conference Series 1994, pp. 43-50.
39. P.J.M. van Laarhoven and E.H.L. Aarts, Simulated Annealing: Theory and Applications, Kluwer Academic Publishers, 1987.
40. P.J.M. van Laarhoven, E.H.L. Aarts and J.K. Lenstra, Job shop scheduling by simulated annealing. Operations Research 40(1)(1992), 113-125.