Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
By:
Cui, Xiaohui ; Potok, Thomas E
Page Number:
12
Book Title:
21st IEEE/ACM International Parallel and Distributed Processing Symposium
Publication Date:
June 2008
Publisher Location:
IEEE/ACM, Long Beach, California, United States of America
Conference Name:
10th International Workshop on Nature Inspired Distributed Computing (NIDISC'07) in conjunction with the 21st IEEE/ACM International Parallel and Distributed Processing Symposium
Conference Location:
Long Beach, California, United States of America
Conference Sponsor:
IEEE/ACM
Abstract
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.
