Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment

Conference · Mon Jan 01 04:00:00 EST 2007

OSTI ID:932608

Cui, Xiaohui ^[1]; Potok, Thomas E ^[1]

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1. ORNL

In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. 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. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 932608
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
COMPUTER NETWORKS
DYNAMICS
OPTIMIZATION
PARALLEL PROCESSING
Particle Swarm Dynamic Optimization
STOCHASTIC PROCESSES