Hybrid Particle Swarm Optimization-differential Evolution Algorithm for Bayesian Network Inference
FAN Rui-xing1，LIU Hao-ran1，ZHANG Li-yue1，SU Zhao-yu1，LIU Bin2
1(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China)2(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China）
Abstract：Aiming at the problem that heuristic algorithm applied to Bayesian network inference learning is easy to fall into local optimum and inefficient in optimization,a new Bayesian network inference learning algorithm based on hybrid particle swarm optimization-difference algorithm (HDPSO-DE) is proposed.The adaptive reverse learning strategy is adopted to increase the diversity of initial population,The differential mutation operator is introduced into particle swarm optimization algorithm,and an adaptive probability hierarchy search strategy is proposed to balance local search and global search.Meanwhile,an adaptive mutation strategy is established according to Levy flight mechanism to avoid the local optimum.The convergence analysis of the proposed algorithm demonstrates that most probable explanation can be found through the iterative search.The experimental results demonstrate that the convergence accuracy and optimization efficiency are improved compared with other algorithms.
2020, Vol. 41 
