A multi-population cooperative particle swarm optimizer for neural network training

This paper presents a new learning algorithm, Multi-Population Cooperative Particle Swarm Optimizer (MCPSO), for neural network training. MCPSO is based on a master-slave model, in which a population consists of a master group and several slave groups. The slave groups execute a single PSO or its variants independently to maintain the diversity of particles, while the master group evolves based on its own information and also the information of the slave groups. The particles both in the master group and the slave groups are co-evolved during the search process by employing a parameter, termed migration factor. The MCPSO is applied for training a multilayer feed-forward neural network, for three benchmark classification problems. The performance of MCPSO used for neural network training is compared to that of Back Propagation (BP), genetic algorithm (GA) and standard PSO (SPSO), demonstrating its effectiveness and efficiency.