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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
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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