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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on (2005)
Towson University, Towson, Maryland, USA
May 23, 2005 to May 25, 2005
ISBN: 0-7695-2294-7
pp: 133-138
X. Cui , Oak Ridge National Laboratory
C. T. Hardin , University of Louisville
R. K. Ragade , University of Louisville
T. E. Potok , Oak Ridge National Laboratory
A. S. Elmaghraby , University of Louisville
ABSTRACT
In the real world, we have to frequently deal with searching for and tracking an optimal solution in a dynamic environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the solution in a dynamic environment. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic environment. In this paper, we present a modified PSO algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing environment.
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CITATION

C. T. Hardin, T. E. Potok, R. K. Ragade, A. S. Elmaghraby and X. Cui, "Tracking Non-Stationary Optimal Solution by Particle Swarm Optimizer," Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on(SNPD-SAWN), Towson University, Towson, Maryland, USA, 2005, pp. 133-138.
doi:10.1109/SNPD-SAWN.2005.77
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