Research on Bayesian Network Structure Learning Based on Hybrid Simplified Particle Swarm Algorithm
LIU Haoran1,2,LI Sheng1,2,CUI Shaopeng1,2,WANG Niantai1,2,CAI Yanbin1,2,SHI Qianrui1,2,ZHANG Liyue1,2
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao, Hebei 066004, China
Abstract：In order to improve the problems that the current Bayesian network structure learning algorithm tends to fall into local optimization, premature convergence and low optimization efficiency, a hybrid simplified particle swarm algorithm is proposed to optimize Bayesian network structure learning(BNs-HsPSO). The algorithm uses the maximum support tree to constrain the search space, and proposes an initial orientation strategy combining V-structure and conditional relative average entropy(CRAE), and then uses the mountain-climbing strategy to establish the initial particle swarm, uses the improved particle swarm optimization algorithm and genetic algorithm to iteratively optimize the initial population, proposes a conditional crossing and mutation strategy in the iterative process to avoid random divergence update of particles, and updates the unoptimized particles in combination with the sub-particle slowing strategy to avoid the algorithm falling into local optimum. The algorithm is compared with other algorithms in simulation experiments under four standard networks.The experimental results show that the proposed algorithm has an average higher BIC score of 5.775%, 5.8%, 0.475%, and 2.75% compared to MMHC, GS, BNC-PSO, and PC-PSO algorithms in ASIA, CAR, CHILD, and ARM networks, respectively; the Hamming distance HD is lower and the accuracy ACC is higher.
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2024, Vol. 45 
