Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05)
Tracking Non-Stationary Optimal Solution by Particle Swarm Optimizer
Towson University, Towson, Maryland, USA
May 23-May 25
ISBN: 0-7695-2294-7
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.
Citation:
X. Cui, C. T. Hardin, R. K. Ragade, T. E. Potok, A. S. Elmaghraby, "Tracking Non-Stationary Optimal Solution by Particle Swarm Optimizer," snpd-sawn, pp.133-138, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05), 2005
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