Optimizing floating centroids method neural network classifier using dynamic multilayer particle swarm optimization

The floating centroids method (FCM) effectively enhances the performance of neural network classifiers. However, the problem of optimizing the neural network continues to restrict the further improvement of FCM. Traditional particle swarm optimization algorithm (PSO) sometimes converges to a local optimal solution in multimodal landscape, particularly for optimizing neural networks. Therefore, the dynamic multilayer PSO (DMLPSO) is proposed to optimize the neural network for improving the performance of FCM. DMLPSO adopts the basic concepts of multi-layer PSO to introduce a dynamic reorganizing strategy, which achieves that valuable information dynamically interacts among different subswarms. This strategy increases population diversity to promote the performance of DMLPSO when optimizing multimodal functions. Experimental results indicate that the proposed DMLPSO enables FCM to obtain improved solutions in many data sets.

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