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Swarm Intelligence

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References

  1. D. K. Agrafiotis and W. Cedeño (2002): Feature selection for structure-activity correlation using binary particle swarms. Journal of Medicinal Chemistry, 45, 1098–1107.

    Article  Google Scholar 

  2. P. J. Angeline (1997): Tracking Extrema in Dynamic Environments. Evolutionary Programming, pp. 335–345.

    Google Scholar 

  3. P. Angeline (1998a): Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, (eds.), Evolutionary Programming VII, 601, 610. Berlin: Springer.

    Google Scholar 

  4. P. J. Angeline (1998b): Using selection to improve particle swarm optimization. IEEE International Conference on Evolutionary Computation, Anchorage, AK, USA.

    Google Scholar 

  5. S. Asch (1956): Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs, 70(9).

    Google Scholar 

  6. T. Bäck (1998): On the behavior of evolutionary algorithms in dynamic environments. In D. B. Fogel, H.-P. Schwefel, Th. Bäck, and X. Yao (eds.), Proc. Fifth IEEE Conference on Evolutionary Computation (ICEC’98), Anchorage AK, pp. 446–451, IEEE Press, Piscataway, NJ.

    Google Scholar 

  7. T. Bäck, F. Hoffmeister, and H. Schwefel (1991): A survey of evolution strategies. In Lashon B. Belew and Richard K. Booker (eds.), Proc. 4th International Conference on Genetic Algorithms, pp. 2–9, San Diego, CA, Morgan Kaufmann.

    Google Scholar 

  8. A. Bandura (1986): Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  9. A. Bavelas (1950): Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22, 727–730.

    Article  Google Scholar 

  10. A. Carlisle and G. Dozier (2000): Adapting particle swarm optimization to dynamic environments. Proc. Int. Conf. Artificial Intelligence, 2000, 429–434, Las Vegas, NV, USA.

    Google Scholar 

  11. A. Carlisle and G. Dozier (2002): Tracking Changing Extrema with Adaptive Particle Swarm Optimizer. ISSCI, 2002 World Automation Congress, Orlando, FL, USA, June, 2002.

    Google Scholar 

  12. W. Cedeño and D. K. Agrafiotis (2003): Using particle swarms for the development of QSAR models based on k-nearest neighbor and kernel regression. Journal of Computer-Aided Molecular Design, 17, 255–263.

    Article  Google Scholar 

  13. R. B. Cialdini (1984): Influence: The Psychology of Persuasion. Quill Publishing.

    Google Scholar 

  14. M. Clerc (1999): The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. Congress on Evolutionary Computation, Washington, D. C., pp. 1951–1957.

    Google Scholar 

  15. M. Clerc and J. Kennedy (2002): The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73.

    Article  Google Scholar 

  16. A. B. Cockshott and B. E. Hartman (2001): Improving the fermentation medium for Echinocandin B production. Part II: Particle swarm optimization. Process Biochemistry, 36, 661–669.

    Google Scholar 

  17. C. A. Coello Coello and S. Lechuga (2001): MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. Technical Report EVOCINV-01-2001, Evolutionary Computation Group at CINVESTAV, Sección de Computación, Departamento de Ingeniería Eléctrica, CINVESTAV-IPN, México.

    Google Scholar 

  18. C. A. Coello Coello and M. S. Lechuga (2002): MOPSO: A proposal for multiple objective particle swarm optimization. IEEE Congress on Evolutionary Computation, 2002, Honolulu, HI, USA.

    Google Scholar 

  19. R. S. Crutchfield (1955): Conformity and character. American Psychologist, 10, 191–198.

    Google Scholar 

  20. R. Dawkins (1989): The Selfish Gene, 2nd ed. Oxford: Oxford University Press.

    Google Scholar 

  21. M. Deutsch and H. B. Gerard (1955): A study of normative and informational social influences upon individual judgment. Journal of Abnormal and Social Psychology, 51, 629–636.

    Google Scholar 

  22. R. C. Eberhart and X. Hu (1999): Human tremor analysis using particle swarm optimization. Proc. Congress on Evolutionary Computation 1999, Washington, D. C. 1927–1930. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  23. R. C. Eberhart and Y. Shi (2000): Comparing inertia weights and constriction factors in particle swarm optimization. Proc. CEC 2000, San Diego, CA, pp. 84–88.

    Google Scholar 

  24. R. C. Eberhart and Y. Shi (1998): Evolving artificial neural networks. Proc. 1998 Int. Conf. Neural Networks and Brain, Beijing, P. R. C., PL5–PL13.

    Google Scholar 

  25. R. C. Eberhart and Y. Shi (2001a): Tracking and optimizing dynamic systems with particle swarms. Proc. Congress on Evolutionary Computation 2001, Seoul, Korea. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  26. R. C. Eberhart and Y. Shi (2001b): Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation 2001, Seoul, Korea. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  27. R. C. Eberhart (2003): Introduction to particle swarm optimization (tutorial). IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA.

    Google Scholar 

  28. A. E. Eiben and C. A. Schippers (1998): On evolutionary exploration and exploitation. Fundamenta Informaticae. IOS Press.

    Google Scholar 

  29. J. E. Fieldsend and S. Singh (2002): A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. Proc. 2002 U.K. Workshop on Computational Intelligence (Birmingham, UK, 2–4 Sept. 2002), pp. 37–44.

    Google Scholar 

  30. L. Festinger (1957): A Theory of Cognitive Dissonance. Evanston IL: Row, Peterson.

    Google Scholar 

  31. L. Festinger (1954/1999): Social communication and cognition: A very preliminary and highly tentative draft. In E. Harmon-Jones and J. Mills (eds.), Cognitive Dissonance: Progress on a Pivotal Theory in Social Psychology. Washington D. C.: AP Publishing.

    Google Scholar 

  32. D. Gies and Y. Rahmat-Samii (2003): Reconfigurable array design using parallel particle swarm optimization. Proceedings of 2003 IEEE Antennas and Propagation Symposium (in press).

    Google Scholar 

  33. F. Heppner and U. Grenander (1990): A stochastic nonlinear model for coordinated bird flocks. In S. Krasner (ed.), The Ubiquity of Chaos. Washington, D. C.: AAAS Publications.

    Google Scholar 

  34. N. Higashi and H. Iba (2003): Particle swarm optimization with gaussian mutation. Proc. IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, IN, USA, pp. 72–79.

    Google Scholar 

  35. J. H. Holland (1975): Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.

    Google Scholar 

  36. C. Hovland (1982): Communication and Persuasion. New York: Greenwood.

    Google Scholar 

  37. X. Hu and R. C. Eberhart (2002a): Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, HI, USA, pp. 1677–1681.

    Google Scholar 

  38. X. Hu and R. C. Eberhart (2002b): Adaptive particle swarm optimization: detection and response to dynamic systems. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA.

    Google Scholar 

  39. X. Hu (2002): Multiobjective optimization using dynamic neighborhood particle swarm optimization. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA.

    Google Scholar 

  40. J. Kennedy (1998): The behavior of particles. Evolutionary Programming VII: Proc. Seventh Annual Conference on Evolutionary Programming, San Diego, CA, pp. 581–589.

    Google Scholar 

  41. J. Kennedy (1999): Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. Proc. Congress on Evolutionary Computation 1999, pp. 1931–1938. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  42. J. Kennedy (2000): Human and Computer Learning Together in the Exteriorized Particle Swarm. Socially Intelligent Agents: The Human in the Loop, pp. 83–89. Technical Report FS-00-04, AAAI Press.

    Google Scholar 

  43. J. Kennedy (2003): Bare bones particle swarms. Proc. IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, IN, USA, 80–87.

    Google Scholar 

  44. J. Kennedy and R. C. Eberhart (1997): A discrete binary version of the particle swarm algorithm. Proc. 1997 Conf. on Systems, Man, and Cybernetics, 4104–4109. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  45. J. Kennedy and R. C. Eberhart (1995): Particle swarm optimization. Proc. IEEE Int. Conf. on Neural Networks, 4, 1942–1948. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  46. J. Kennedy and R. Mendes (2002): Population structure and particle swarm performance. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA.

    Google Scholar 

  47. J. Kennedy and R. Mendes (2003): Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. In Proc. 2003 IEEE SMC Workshop on Soft Computing in Industrial Applications (SMCia03), Binghamton, NY.

    Google Scholar 

  48. J. Kennedy and W. M. Spears (1998): Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. Proc. Int. Conf. on Evolutionary Computation, pp. 78–83. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  49. B. Latané (1981): The psychology of social impact. American Psychologist, 36, 343–356.

    Google Scholar 

  50. J. M. Levine, L. B. Resnick, and E. T. Higgins (1993): Social foundations of cognition. Annual Review of Psychology, 44, 585–612.

    Google Scholar 

  51. E. F. Loftus and K. Ketcham (1994): The Myth of Repressed Memory: False Memories and Allegations of Sexual Abuse. New York: St. Martin’s Press.

    Google Scholar 

  52. C. K. Mohan and B. Al-kazemi (2001): Discrete particle swarm optimization. Proc. Workshop on Particle Swarm Optimization. Indianapolis, IN: Purdue School of Engineering and Technology, IUPUI (in press).

    Google Scholar 

  53. S. Naka, T. Genji, K. Miyazato, and Y. Fukuyama (2002): Hybrid particle swarm optimization based distribution state estimation using constriction factor approach. Proc. Joint 1st International Conference on Soft Computing and Intelligent Systems and 3rd International Symposium on Advanced Intelligent Systems (SCIS & ISIS).

    Google Scholar 

  54. A. Newell and H. Simon (1963): GPS: A program that simulates human thought. In Feigenbaum and Feldman. (ed.), Computers and Thought. McGraw-Hill, New York.

    Google Scholar 

  55. R. E. Nisbett and D. W. Wilson (1977): Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259.

    Google Scholar 

  56. A. Nowak, J. Szamrej, and B. Latané (1990): From private attitude to public opinion: A dynamic theory of social impact. Psychological Review, 97, 362–376.

    Article  Google Scholar 

  57. E. Ozcan and C. Mohan (1999): Particle swarm optimization: surfing the waves. Proc. 1999 Congress on Evolutionary Computation, 1939–1944. Piscataway, NJ: IEEE Service Center.

    Google Scholar 

  58. E. Ozcan and C. K. Mohan (1998): Analysis of a simple particle swarm optimization system. Intelligent Engineering Systems Through Artificial Neural Networks, 8, 253–258.

    Google Scholar 

  59. K. E. Parsopoulos and M. N. Vrahatis (2002a): Particle swarm optimization method in multiobjective problems, Proceedings of the 2002 ACM Symposium on Applied Computing (SAC 2002), pp. 603–607.

    Google Scholar 

  60. K. E. Parsopoulos and M. N. Vrahatis (2002b): Initializing the particle swarm optimizer using the nonlinear simplex method. In A. Grmela and N. E. Mastorakis (eds), Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 216–221. WSEAS Press.

    Google Scholar 

  61. R. E. Petty and J. T. Cacioppo (1981): Attitudes and persuasion: Classic and contemporary approaches. Dubuque, IA: Wm. C. Brown.

    Google Scholar 

  62. K. V. Price (1999): An introduction to differential evolution. In D.W. Corne, M. Dorigo, F. Glover (eds), New Ideas in Optimization. McGraw Hill.

    Google Scholar 

  63. R. G. Reynolds (1994): An introduction to cultural algorithms. Proc. Third Annual Conference on Evolutionary Programming, pp. 131–139.

    Google Scholar 

  64. C. W. Reynolds (1987): Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21, 25–34.

    Google Scholar 

  65. B. R. Secrest and G. B. Lamont (2003): Visualizing particle swarm optimization—gaussian particle swarm optimization. Proc. IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, IN, USA, pp. 198–204.

    Google Scholar 

  66. J. D. Schaffer (1985): Multiple objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100.

    Google Scholar 

  67. M. Sherif (1936): The Psychology Of Social Norms. New York: Harper Brothers.

    Google Scholar 

  68. Y. Shi and R. C. Eberhart (1998): Parameter selection in particle swarm optimization. Proc. Seventh Annual Conference on Evolutionary Programming, pp. 591–601.

    Google Scholar 

  69. M. Tomasello (1999): The Cultural Origins of Human Cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  70. S. Ujjin and P. J. Bentley (2003): Particle swarm optimization recommender system. In Proc. IEEE Swarm Intelligence Symposium 2003, Indianapolis, IN, USA.

    Google Scholar 

  71. D. Watts and S. Strogatz (1998): Collective dynamics of small-world networks. Nature, 363:202–204.

    Google Scholar 

  72. D. M. Wegner (2002): The Illusion of Conscious Will. Cambridge, MA: The MIT Press.

    Google Scholar 

  73. K. Weinert, J. Mehnen, and G. Rudolph (2001): Dynamic Neighborhood Structures in Parallel Evolution Strategies (Technical Report). Reihe CI 112/01, SFB 531, University of Dortmund.

    Google Scholar 

  74. S. Wolfram (1994): Cellular Automata and Complexity: Collected Papers. Reading, MA: Addison-Wesley.

    Google Scholar 

  75. X. Xiao, R. Dow, R. C. Eberhart, B. Miled, and R. J. Oppelt (2003): Gene clustering using self-organizing maps and particle swarm optimization. Second IEEE International Workshop on High Performance Computational Biology, Nice, France.

    Google Scholar 

  76. H. Yoshida, Y. Fukuyama, S. Takayama, and Y. Nakanishi (1999): A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment. 1999 IEEE International Conference on Systems, Man, and Cybernetics, 6, 502.

    Google Scholar 

  77. W. J. Zhang and X. F. Xie (2003): DEPSO: hybrid particle swarm with differential evolution operator. IEEE Int. Conf. on Systems, Man & Cybernetics (SMCC), Washington, D. C. USA.

    Google Scholar 

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Kennedy, J. (2006). Swarm Intelligence. In: Zomaya, A.Y. (eds) Handbook of Nature-Inspired and Innovative Computing. Springer, Boston, MA. https://doi.org/10.1007/0-387-27705-6_6

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