Bayesian network structure learning algorithm based on hybrid binary salp swarm-differential evolution algorithm - Bin LIU; Ruixing FAN; Haoran LIU; Liyue ZHANG; Haiyu WANG; Chunlan ZHANG (Tongxin xuebao)
Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms,which were trapping in local minimums and having low search efficiency,a method of learning Bayesian network structure based on hybrid binary slap swarm-differential evolution algorithm was proposed.An adaptive scale factor was used to balance local and global search in the swarm grouping stage.The improved mutation operator and crossover operator were taken into salp search strategy and differential search strategy respectively to renew different subswarms in the update stage.Two-point mutation operator was adopted to improve the swarm’s diversity in the stage of merging of subswarms.The convergence analysis of the proposed algorithm demonstrates that best structure can be found through the iterative search of population.Experimental results show that the convergence accuracy and efficiency of the proposed algorithm are improved compared with other algorithms.