Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA-PSO-NN)

In this short paper, a coupled genetic algorithm and particle swarm optimization technique was used to supervise neural networks where the applied operators and connections of layers were tracked by genetic algorithm and numeric values of biases and weights of layers were examined by particle swarm optimization to modify the optimal network topology. The method was applied for a previously studied case, and results were analyzed. The convergence to the optimal topology was highly fast and efficient, and the obtained weights and biases revealed great reliability in reproduction of data. The optimal topology of neural networks was obtained only after seven iterations, and an average square of the correlation (R-2) of 0.9989 was obtained for the studied cases. The proposed method can be used for fast and reliable topology optimization of neural networks.