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This paper proposed a hybrid evolutionary system HPSONN to automatically design artificial neural networks (ANNpsilas), where ANNpsilas structure and parameters are tuned simultaneously. In HPSONN, an improved particle swarm optimization using optimal foraging theory (PSOOFT) and a binary particle swarm optimization (BPSO) are used to train ANNpsilas free parameters (weights and bias) and find optimal...
Inspired by the phenomenon of chemotaxis in colonies of the bacteria, an improved particle swarm optimization (PSO) is presented by analogy to the way that bacteria react to chemo-attractants or chemo-repellents. The proposed algorithm (PSOBC) alternates between phases of attraction and repulsion. Once the diversity of population is too low, the individuals will be dispersed by repulsion force, while...
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