Abstract
This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment. One possible application of this research is building scientific understanding of the insurgency in the count-Insurgent warfare.
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References
Bonabeau E., Dorigo M., and Theraulaz G.: Swarm intelligence from natural to artificial systems. Oxford University Press, New York, NY (1999)
Eberhart R. and Kennedy J.: A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995) 39–43
Kennedy J.: The particle swarm: social adaptation of knowledge. In Proceedings of International Conference on Evolutionary Computation, Indianapolis, IN, USA (1997) 303–308
Kennedy J., Eberhart R. C., and Shi Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Cecilia D. C., Riccardo P., and Paolo D. C.: Modelling Group-Foraging Behaviour with Particle Swarms. Lecture Notes in Computer Science, vol. 4193/2006, (2006) 661–670
Anthony B., Arlindo S., Tiago S., Michael O. N., Robin M., and Ernesto C.: A Particle Swarm Model of Organizational Adaptation. In Genetic and Evolutionary Computation (GECCO), Seattle, WA, USA (2004) 12–23
Silva A. S., Tiago F., Michael O. N., Robin M., and Ernesto C.: Investigating Strategic Inertia Using OrgSwarm. Informatica, vol. 29, (2005) 125–141
Clerc M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, USA (1999) 1951–1957
Clerc M. and Kennedy J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, vol. 6 (2002) 58–73
Morrison R. W. and DeJong K. A.: A test problem generator for non-stationary environments. In Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, USA (1999) 2047–2053
Angeline P. J.: Tracking extrema in dynamic environments. In Angeline, Reynolds, McDonnell and Eberhart (Eds.), Proc. of the 6th Int. Conf. on Evolutionary Programming, LNCS, Vol. 1213, Springer, (1997) 335–345
Blackwell T. and Branke J.: Multi-swarm optimization in dynamic environments. Applications of Evolutionary Computing, LNCS, Vol 3005, Springer, (2004) 489–500
Eberhart R. C. and Shi Y.: Tracking and optimizing dynamic systems with particle swarms. In Proceedings of Congress on Evolutionary Computation, Seoul, South Korea (2001) 94–100
Parsopoulos K. E. and Vrahatis M. N.: Recent approaches to global optimization problems through particle swarm optimization. Natural Computing (2002) 1 235–306
Cui X., Hardin C. T., Ragade R. K., Potok T. E., and Elmaghraby A. S.: Tracking non-stationary optimal solution by particle swarm optimizer. In Proceedings of 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing, Towson, MD, USA (2005) 133–138
Tisue S.: NetLogo: A Simple Environment for Modeling Complexity. In International Conference on Complex Systems, Boston, MA (2004)
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Cui, X., Patton, R.M., Treadwell, J., Potok, T.E. (2008). Particle Swarm Based Collective Searching Model for Adaptive Environment. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_20
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DOI: https://doi.org/10.1007/978-3-540-78987-1_20
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