Particle swarm optimization with thresheld convergence
Date
2013
Authors
Chen, Stephen
Montgomery, James
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IEEE
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.
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Keywords
particle swarm optimization, thresheld convergence, niching, crowding, exploration, exploitation, multi-modal optimization
Citation
Chen, S. & Montgomery, J. (2013). Particle swarm optimization with thresheld convergence. Paper to be presented at IEEE Congress on Evolutionary Computation (CEC2013), June 20-23, 2013, Cancun, Mexico.
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2013 IEEE Congress on Evolutionary Computation
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Conference paper
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