Particle swarm optimization with thresheld convergence
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...[Show more]
|Collections||ANU Research Publications|
|Source:||2013 IEEE Congress on Evolutionary Computation|
|Chen_ParticleSwarm2013.pdf||461.26 kB||Adobe PDF|
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