基于复杂网络社区检测算法的元器件选用推荐方法OA

The selection of space components is crucial in the space mission.The space environment is complex and harsh,imposing ex-tremely high requirements on the reliability and performance of aerospace components.Traditional component selection methods usually rely on expert experience and a single index evaluation,making it difficult to fully consider the complex correlation and multi-dimensional performance specifications between components.The development of complex network theory provides a new approach for the compo-nent selection.In particular,community detection algorithms can help identify the potential relationships and group characteristics a-mong components,thereby optimizing the selection process and achieving precise,rapid,efficient,and flexible selection of aerospace components.In this paper,we will introduce the selection recommendation method for component selection based on complex network community detection algorithm,and propose the evolutionary algorithm based on module degree optimization.The algorithm incorpo-rates a maximum spanning tree coding method based on node similarity,a new method for generating initial populations and a sine-based adaptive variation function,and applies it to two component selection networks.The algorithm effectively detects the community struc-ture in the component selection networks,and realizes the intelligent selection of components.