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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:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Montgomery, J (Dr James Montgomery)
ID Code:92141
Year Published:2013
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2014-06-06
Last Modified:2016-01-19
Downloads:0

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