Tracking Non-Stationary Optimal Solution by Particle Swarm Optimizer

Authors:
X. Cui;C. T. Hardin;R. K. Ragade;T. E. Potok;A. S. Elmaghraby
Affiliations:
Oak Ridge National Laboratory;University of Louisville;University of Louisville;Oak Ridge National Laboratory;University of Louisville
Venue:
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Year:
2005

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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.