Learning a simple multilayer perceptron with PSO

抄録

In this study, we attempt to learn the parameters of a multilayer perceptron (MLP) using the particle swarm optimization (PSO) method, which is an approximate solution method for optimization problems without requiring the derivative information of the evaluation function. We used the gradient method and PSO to learn to classify a linearly inseparable dataset with an MLP in the middle layer with a few neurons. We experimentally confirmed that PSO outperformed gradient-based learning.