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Particle swarm optimization with thresheld convergence
Citation
Chen, S and Montgomery, J, Particle swarm optimization with thresheld convergence, Proceedings of the 2013 IEEE Congress on Evolutionary Computation, 20-23 June 2013, Cancun, Mexico, pp. 510-516. ISBN 978-1-4799-0453-2 (2013) [Refereed Conference Paper]
Copyright Statement
Copyright 2012 IEEE
DOI: doi:10.1109/CEC.2013.6557611
Abstract
Many heuristic search techniques have concurrent
processes of exploration and exploitation. In particle swarm
optimization, an improved pbest position can represent a new
more promising region of the search space (exploration) or a
better solution within the current region (exploitation). The latter
can interfere with the former since the identification of a new
more promising region depends on finding a (random) solution in
that region which is better than the current pbest. Ideally, every
sampled solution will have the same relative fitness with respect
to its nearby local optimum – finding the best region to exploit
then becomes the problem of finding the best random solution.
However, a locally optimized solution from a poor region of the
search space can be better than a random solution from a good
region of the search space. Since exploitation can interfere with
subsequent/concurrent exploration, it should be prevented during
the early stages of the search process. In thresheld convergence,
early exploitation is "held" back by a threshold function.
Experiments show that the addition of thresheld convergence to
particle swarm optimization can lead to large performance
improvements in multi-modal search spaces.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | particle swarm optimization, thresheld convergence, crowding, exploration, exploitation |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the information and computing sciences |
UTAS Author: | Montgomery, J (Dr James Montgomery) |
ID Code: | 92141 |
Year Published: | 2013 |
Web of Science® Times Cited: | 12 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2014-06-06 |
Last Modified: | 2018-04-07 |
Downloads: | 0 |
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