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In view of accuracy problem of standard Bayesian algorithm in risk assessment model, this paper proposes a kind of optimized one based on task decomposition for complex product system risk assessment. Firstly, chaotic mapping is used to introduce the chaotic states into the optimal variables in artificial fish swarm algorithm, and the ergodicity range of chaotic motion is extended to the range of optimal variables value. Then the searching is optimized by chaotic variables. The task of Bayesian network is decomposed by the improved artificial fish algorithm accompanied by the construction of evaluation function to improve the accuracy. The simulation experiment shows the improved artificial fish swarm algorithm has better convergence and higher accuracy in complex product system risk assessment.

Complex Product System Risk Assessment Model Based on Bayesian Algorithm Optimized By Task Decomposition