关键词: 贝叶斯网络, 粒子群优化, 适应度函数, 结构学习, 符号编码

Abstract: A discrete PSO（Particle Swarm Optimization） based Bayesian network structure learning algorithm—PSBN（Particle Swarm for Bayesian Network） is proposed.A fitness function is given to evaluate the possible BN structure.Based on the characteristics of BN structure，the definition and encoding of the position and velocity of particle in PSO are given，and the basic operations of PSO are designed，which provides guarantee of convergence.As BN structure is considered as a symbol encoding，the BN structure having higher fitness values can be gotten by changing the symbol encoding of particles.The experimental results show this algorithm has better performance than the BN structure learning algorithm based on genetic algorithm.

Key words: Bayesian network, particle swarm optimization, fitness function, structure learning, symbol encoding