Particle swarm optimization with thresheld convergence

  • Chen S
  • Montgomery J
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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. © 2013 IEEE.

Author-supplied keywords

  • crowding
  • exploitation
  • exploration
  • niching
  • particle swarm optimization
  • thresheld convergence

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  • Stephen Chen

  • James Montgomery

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