A Parallel Distributed Structure Learning Method of Dynamic Bayesian Networks Using Discrete Particle Swarm Optimization

摘要

In this paper, we present a multi-objective Discrete Particle Optimizer (DPSO) for the parallel distributed learning of Dynamic Bayesian Network (DBN) structures. The proposed method introduces a hierarchical structure consisting of DPSOs and a Multi-Objective Genetic Algorithm (MOGA). Groups of DPSOs in the lower layer find effective DBN sub-network structures and a group of MOGA in the higher layer finds whole of the DBN network structure. Through numerical simulations, we show that the proposed method can find more effective DBN structures and can obtain them faster than the conventional method.